Science.gov

Sample records for control multipotent neural

  1. Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates.

    PubMed

    Mundell, Nathan A; Labosky, Patricia A

    2011-02-01

    Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency. PMID:21228004

  2. Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates

    PubMed Central

    Mundell, Nathan A.; Labosky, Patricia A.

    2011-01-01

    Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency. PMID:21228004

  3. Premigratory and migratory neural crest cells are multipotent in vivo.

    PubMed

    Baggiolini, Arianna; Varum, Sandra; Mateos, José María; Bettosini, Damiano; John, Nessy; Bonalli, Mario; Ziegler, Urs; Dimou, Leda; Clevers, Hans; Furrer, Reinhard; Sommer, Lukas

    2015-03-01

    The neural crest (NC) is an embryonic stem/progenitor cell population that generates a diverse array of cell lineages, including peripheral neurons, myelinating Schwann cells, and melanocytes, among others. However, there is a long-standing controversy as to whether this broad developmental perspective reflects in vivo multipotency of individual NC cells or whether the NC is comprised of a heterogeneous mixture of lineage-restricted progenitors. Here, we resolve this controversy by performing in vivo fate mapping of single trunk NC cells both at premigratory and migratory stages using the R26R-Confetti mouse model. By combining quantitative clonal analyses with definitive markers of differentiation, we demonstrate that the vast majority of individual NC cells are multipotent, with only few clones contributing to single derivatives. Intriguingly, multipotency is maintained in migratory NC cells. Thus, our findings provide definitive evidence for the in vivo multipotency of both premigratory and migrating NC cells in the mouse. PMID:25748934

  4. Crestospheres: Long-Term Maintenance of Multipotent, Premigratory Neural Crest Stem Cells

    PubMed Central

    Kerosuo, Laura; Nie, Shuyi; Bajpai, Ruchi; Bronner, Marianne E.

    2015-01-01

    Summary Premigratory neural crest cells comprise a transient, embryonic population that arises within the CNS, but subsequently migrates away and differentiates into many derivatives. Previously, premigratory neural crest could not be maintained in a multipotent, adhesive state without spontaneous differentiation. Here, we report conditions that enable maintenance of neuroepithelial “crestospheres” that self-renew and retain multipotency for weeks. Moreover, under differentiation conditions, these cells can form multiple derivatives in vitro and in vivo after transplantation into chick embryos. Similarly, human embryonic stem cells directed to a neural crest fate can be maintained as crestospheres and subsequently differentiated into several derivatives. By devising conditions that maintain the premigratory state in vitro, these results demonstrate that neuroepithelial neural crest precursors are capable of long-term self-renewal. This approach will help uncover mechanisms underlying their developmental potential, differentiation and, together with the induced pluripotent stem cell techniques, the pathology of human neurocristopathies. PMID:26441305

  5. Crestospheres: Long-Term Maintenance of Multipotent, Premigratory Neural Crest Stem Cells.

    PubMed

    Kerosuo, Laura; Nie, Shuyi; Bajpai, Ruchi; Bronner, Marianne E

    2015-10-13

    Premigratory neural crest cells comprise a transient, embryonic population that arises within the CNS, but subsequently migrates away and differentiates into many derivatives. Previously, premigratory neural crest could not be maintained in a multipotent, adhesive state without spontaneous differentiation. Here, we report conditions that enable maintenance of neuroepithelial "crestospheres" that self-renew and retain multipotency for weeks. Moreover, under differentiation conditions, these cells can form multiple derivatives in vitro and in vivo after transplantation into chick embryos. Similarly, human embryonic stem cells directed to a neural crest fate can be maintained as crestospheres and subsequently differentiated into several derivatives. By devising conditions that maintain the premigratory state in vitro, these results demonstrate that neuroepithelial neural crest precursors are capable of long-term self-renewal. This approach will help uncover mechanisms underlying their developmental potential, differentiation and, together with the induced pluripotent stem cell techniques, the pathology of human neurocristopathies. PMID:26441305

  6. Methods for derivation of multipotent neural crest cells derived from human pluripotent stem cells

    PubMed Central

    Avery, John; Dalton, Stephen

    2016-01-01

    Summary Multipotent, neural crest cells (NCCs) produce a wide-range of cell types during embryonic development. This includes melanocytes, peripheral neurons, smooth muscle cells, osteocytes, chondrocytes and adipocytes. The protocol described here allows for highly-efficient differentiation of human pluripotent stem cells to a neural crest fate within 15 days. This is accomplished under feeder-free conditions, using chemically defined medium supplemented with two small molecule inhibitors that block glycogen synthase kinase 3 (GSK3) and bone morphogenic protein (BMP) signaling. This technology is well-suited as a platform to understand in greater detail the pathogenesis of human disease associated with impaired neural crest development/migration. PMID:25986498

  7. Exclusive multipotency and preferential asymmetric divisions in post-embryonic neural stem cells of the fish retina

    PubMed Central

    Centanin, Lázaro; Ander, Janina-J.; Hoeckendorf, Burkhard; Lust, Katharina; Kellner, Tanja; Kraemer, Isabel; Urbany, Cedric; Hasel, Eva; Harris, William A.; Simons, Benjamin D.; Wittbrodt, Joachim

    2014-01-01

    The potency of post-embryonic stem cells can only be addressed in the living organism, by labeling single cells after embryonic development and following their descendants. Recently, transplantation experiments involving permanently labeled cells revealed multipotent neural stem cells (NSCs) of embryonic origin in the medaka retina. To analyze whether NSC potency is affected by developmental progression, as reported for the mammalian brain, we developed an inducible toolkit for clonal labeling and non-invasive fate tracking. We used this toolkit to address post-embryonic stem cells in different tissues and to functionally differentiate transient progenitor cells from permanent, bona fide stem cells in the retina. Using temporally controlled clonal induction, we showed that post-embryonic retinal NSCs are exclusively multipotent and give rise to the complete spectrum of cell types in the neural retina. Intriguingly, and in contrast to any other vertebrate stem cell system described so far, long-term analysis of clones indicates a preferential mode of asymmetric cell division. Moreover, following the behavior of clones before and after external stimuli, such as injuries, shows that NSCs in the retina maintained the preference for asymmetric cell division during regenerative responses. We present a comprehensive analysis of individual post-embryonic NSCs in their physiological environment and establish the teleost retina as an ideal model for studying adult stem cell biology at single cell resolution. PMID:25142461

  8. FGF8 signaling sustains progenitor status and multipotency of cranial neural crest-derived mesenchymal cells in vivo and in vitro.

    PubMed

    Shao, Meiying; Liu, Chao; Song, Yingnan; Ye, Wenduo; He, Wei; Yuan, Guohua; Gu, Shuping; Lin, Congxin; Ma, Liang; Zhang, Yanding; Tian, Weidong; Hu, Tao; Chen, YiPing

    2015-10-01

    The cranial neural crest (CNC) cells play a vital role in craniofacial development and regeneration. They are multi-potent progenitors, being able to differentiate into various types of tissues. Both pre-migratory and post-migratory CNC cells are plastic, taking on diverse fates by responding to different inductive signals. However, what sustains the multipotency of CNC cells and derivatives remains largely unknown. In this study, we present evidence that FGF8 signaling is able to sustain progenitor status and multipotency of CNC-derived mesenchymal cells both in vivo and in vitro. We show that augmented FGF8 signaling in pre-migratory CNC cells prevents cell differentiation and organogenesis in the craniofacial region by maintaining their progenitor status. CNC-derived mesenchymal cells with Fgf8 overexpression or control cells in the presence of exogenous FGF8 exhibit prolonged survival, proliferation, and multi-potent differentiation capability in cell cultures. Remarkably, exogenous FGF8 also sustains the capability of CNC-derived mesenchymal cells to participate in organogenesis such as odontogenesis. Furthermore, FGF8-mediated signaling strongly promotes adipogenesis but inhibits osteogenesis of CNC-derived mesenchymal cells in vitro. Our results reveal a specific role for FGF8 in the maintenance of progenitor status and in fate determination of CNC cells, implicating a potential application in expansion and fate manipulation of CNC-derived cells in stem cell-based craniofacial regeneration. PMID:26243590

  9. Neural differentiation of novel multipotent progenitor cells from cryopreserved human umbilical cord blood

    SciTech Connect

    Lee, Myoung Woo; Moon, Young Joon; Yang, Mal Sook; Kim, Sun Kyung; Jang, In Keun; Eom, Young-woo; Park, Joon Seong; Kim, Hugh C.; Song, Kye Yong; Park, Soon Cheol; Lim, Hwan Sub; Kim, Young Jin . E-mail: jin@lifecord.co.kr

    2007-06-29

    Umbilical cord blood (UCB) is a rich source of hematopoietic stem cells, with practical and ethical advantages. To date, the presence of other stem cells in UCB remains to be established. We investigated whether other stem cells are present in cryopreserved UCB. Seeded mononuclear cells formed adherent colonized cells in optimized culture conditions. Over a 4- to 6-week culture period, colonized cells gradually developed into adherent mono-layer cells, which exhibited homogeneous fibroblast-like morphology and immunophenotypes, and were highly proliferative. Isolated cells were designated 'multipotent progenitor cells (MPCs)'. Under appropriate conditions for 2 weeks, MPCs differentiated into neural tissue-specific cell types, including neuron, astrocyte, and oligodendrocyte. Differentiated cells presented their respective markers, specifically, NF-L and NSE for neurons, GFAP for astrocytes, and myelin/oligodendrocyte for oligodendrocytes. In this study, we successfully isolated MPCs from cryopreserved UCB, which differentiated into the neural tissue-specific cell types. These findings suggest that cryopreserved human UCB is a useful alternative source of neural progenitor cells, such as MPCs, for experimental and therapeutic applications.

  10. Isolation of Novel Multipotent Neural Crest-Derived Stem Cells from Adult Human Inferior Turbinate

    PubMed Central

    Hauser, Stefan; Widera, Darius; Qunneis, Firas; Müller, Janine; Zander, Christin; Greiner, Johannes; Strauss, Christina; Lüningschrör, Patrick; Heimann, Peter; Schwarze, Hartmut; Ebmeyer, Jörg; Sudhoff, Holger; Araúzo-Bravo, Marcos J.; Greber, Boris; Zaehres, Holm; Schöler, Hans; Kaltschmidt, Christian

    2012-01-01

    Adult human neural crest-derived stem cells (NCSCs) are of extraordinary high plasticity and promising candidates for the use in regenerative medicine. Here we describe for the first time a novel neural crest-derived stem cell population within the respiratory epithelium of human adult inferior turbinate. In contrast to superior and middle turbinates, high amounts of source material could be isolated from human inferior turbinates. Using minimally-invasive surgery methods isolation is efficient even in older patients. Within their endogenous niche, inferior turbinate stem cells (ITSCs) expressed high levels of nestin, p75NTR, and S100. Immunoelectron microscopy using anti-p75 antibodies displayed that ITSCs are of glial origin and closely related to nonmyelinating Schwann cells. Cultivated ITSCs were positive for nestin and S100 and the neural crest markers Slug and SOX10. Whole genome microarray analysis showed pronounced differences to human ES cells in respect to pluripotency markers OCT4, SOX2, LIN28, and NANOG, whereas expression of WDR5, KLF4, and c-MYC was nearly similar. ITSCs were able to differentiate into cells with neuro-ectodermal and mesodermal phenotype. Additionally ITSCs are able to survive and perform neural crest typical chain migration in vivo when transplanted into chicken embryos. However ITSCs do not form teratomas in severe combined immunodeficient mice. Finally, we developed a separation strategy based on magnetic cell sorting of p75NTR positive ITSCs that formed larger neurospheres and proliferated faster than p75NTR negative ITSCs. Taken together our study describes a novel, readily accessible source of multipotent human NCSCs for potential cell-replacement therapy. PMID:22128806

  11. Clonal identification of multipotent precursors from adult mouse pancreas that generate neural and pancreatic lineages.

    PubMed

    Seaberg, Raewyn M; Smukler, Simon R; Kieffer, Timothy J; Enikolopov, Grigori; Asghar, Zeenat; Wheeler, Michael B; Korbutt, Gregory; van der Kooy, Derek

    2004-09-01

    The clonal isolation of putative adult pancreatic precursors has been an elusive goal of researchers seeking to develop cell replacement strategies for diabetes. We report the clonal identification of multipotent precursor cells from the adult mouse pancreas. The application of a serum-free, colony-forming assay to pancreatic cells enabled the identification of precursors from pancreatic islet and ductal populations. These cells proliferate in vitro to form clonal colonies that coexpress neural and pancreatic precursor markers. Upon differentiation, individual clonal colonies produce distinct populations of neurons and glial cells, pancreatic endocrine beta-, alpha- and delta-cells, and pancreatic exocrine and stellate cells. Moreover, the newly generated beta-like cells demonstrate glucose-dependent Ca(2+) responsiveness and insulin release. Pancreas colonies do not express markers of embryonic stem cells, nor genes suggestive of mesodermal or neural crest origins. These cells represent a previously unidentified adult intrinsic pancreatic precursor population and are a promising candidate for cell-based therapeutic strategies. PMID:15322557

  12. Extracellular matrix-regulated neural differentiation of human multipotent marrow progenitor cells enhances functional recovery after spinal cord injury

    PubMed Central

    Deng, Win-Ping; Yang, Chi-Chiang; Yang, Liang-Yo; Chen, Chun-Wei D.; Chen, Wei-Hong; Yang, Charn-Bing; Chen, Yu-Hsin; Lai, Wen-Fu T.; Renshaw, Perry F.

    2015-01-01

    BACKGROUND CONTEXT Recent advanced studies have demonstrated that cytokines and extracellular matrix (ECM) could trigger various types of neural differentiation. However, the efficacy of differentiation and in vivo transplantation has not yet thoroughly been investigated. PURPOSE To highlight the current understanding of the effects of ECM on neural differentiation of human bone marrow-derived multipotent progenitor cells (MPCs), regarding state-of-art cure for the animal with acute spinal cord injury (SCI), and explore future treatments aimed at neural repair. STUDY DESIGN A selective overview of the literature pertaining to the neural differentiation of the MSCs and experimental animals aimed at improved repair of SCI. METHODS Extracellular matrix proteins, tenascin-cytotactin (TN-C), tenascin-restrictin (TN-R), and chondroitin sulfate (CS), with the cytokines, nerve growth factor (NGF)/brain-derived neurotrophic factor (BDNF)/retinoic acid (RA) (NBR), were incorporated to induce transdifferentiation of human MPCs. Cells were treated with NBR for 7 days, and then TN-C, TN-R, or CS was added for 2 days. The medium was changed every 2 days. Twenty-four animals were randomly assigned to four groups with six animals in each group: one experimental and three controls. Animals received two (bilateral) injections of vehicle, MPCs, NBR-induced MPCs, or NBR/TN-C-induced MPCs into the lesion sites after SCI. Functional assessment was measured using the Basso, Beattie, and Bresnahan locomotor rating score. Data were analyzed using analysis of variance followed by Student-Newman-Keuls (SNK) post hoc tests. RESULTS Results showed that MPCs with the transdifferentiation of human MPCs to neurons were associated with increased messenger-RNA (mRNA) expression of neuronal markers including nestin, microtubule-associated protein (MAP) 2, glial fibrillary acidic protein, βIII tubulin, and NGF. Greater amounts of neuronal morphology appeared in cultures incorporated with TN-C and TN

  13. Fuzzy and neural control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  14. Sensitive Tumorigenic Potential Evaluation of Adult Human Multipotent Neural Cells Immortalized by hTERT Gene Transduction

    PubMed Central

    Jeong, Da Eun; Kim, Sung Soo; Song, Hye Jin; Pyeon, Hee Jang; Kang, Kyeongjin; Hong, Seung-Cheol; Nam, Do-Hyun; Joo, Kyeung Min

    2016-01-01

    Stem cells and therapeutic genes are emerging as a new therapeutic approach to treat various neurodegenerative diseases with few effective treatment options. However, potential formation of tumors by stem cells has hampered their clinical application. Moreover, adequate preclinical platforms to precisely test tumorigenic potential of stem cells are controversial. In this study, we compared the sensitivity of various animal models for in vivo stem cell tumorigenicity testing to identify the most sensitive platform. Then, tumorigenic potential of adult human multipotent neural cells (ahMNCs) immortalized by the human telomerase reverse transcriptase (hTERT) gene was examined as a stem cell model with therapeutic genes. When human glioblastoma (GBM) cells were injected into adult (4–6-week-old) Balb/c-nu, adult NOD/SCID, adult NOG, or neonate (1–2-week-old) NOG mice, the neonate NOG mice showed significantly faster tumorigenesis than that of the other groups regardless of intracranial or subcutaneous injection route. Two kinds of ahMNCs (682TL and 779TL) were primary cultured from surgical samples of patients with temporal lobe epilepsy. Although the ahMNCs were immortalized by lentiviral hTERT gene delivery (hTERT-682TL and hTERT-779TL), they did not form any detectable masses, even in the most sensitive neonate NOG mouse platform. Moreover, the hTERT-ahMNCs had no gross chromosomal abnormalities on a karyotype analysis. Taken together, our data suggest that neonate NOG mice could be a sensitive animal platform to test tumorigenic potential of stem cell therapeutics and that ahMNCs could be a genetically stable stem cell source with little tumorigenic activity to develop regenerative treatments for neurodegenerative diseases. PMID:27391353

  15. Sensitive Tumorigenic Potential Evaluation of Adult Human Multipotent Neural Cells Immortalized by hTERT Gene Transduction.

    PubMed

    Lee, Kee Hang; Nam, Hyun; Jeong, Da Eun; Kim, Sung Soo; Song, Hye Jin; Pyeon, Hee Jang; Kang, Kyeongjin; Hong, Seung-Cheol; Nam, Do-Hyun; Joo, Kyeung Min

    2016-01-01

    Stem cells and therapeutic genes are emerging as a new therapeutic approach to treat various neurodegenerative diseases with few effective treatment options. However, potential formation of tumors by stem cells has hampered their clinical application. Moreover, adequate preclinical platforms to precisely test tumorigenic potential of stem cells are controversial. In this study, we compared the sensitivity of various animal models for in vivo stem cell tumorigenicity testing to identify the most sensitive platform. Then, tumorigenic potential of adult human multipotent neural cells (ahMNCs) immortalized by the human telomerase reverse transcriptase (hTERT) gene was examined as a stem cell model with therapeutic genes. When human glioblastoma (GBM) cells were injected into adult (4-6-week-old) Balb/c-nu, adult NOD/SCID, adult NOG, or neonate (1-2-week-old) NOG mice, the neonate NOG mice showed significantly faster tumorigenesis than that of the other groups regardless of intracranial or subcutaneous injection route. Two kinds of ahMNCs (682TL and 779TL) were primary cultured from surgical samples of patients with temporal lobe epilepsy. Although the ahMNCs were immortalized by lentiviral hTERT gene delivery (hTERT-682TL and hTERT-779TL), they did not form any detectable masses, even in the most sensitive neonate NOG mouse platform. Moreover, the hTERT-ahMNCs had no gross chromosomal abnormalities on a karyotype analysis. Taken together, our data suggest that neonate NOG mice could be a sensitive animal platform to test tumorigenic potential of stem cell therapeutics and that ahMNCs could be a genetically stable stem cell source with little tumorigenic activity to develop regenerative treatments for neurodegenerative diseases. PMID:27391353

  16. Neural control of muscle

    NASA Technical Reports Server (NTRS)

    Max, S. R.; Markelonis, G. J.

    1983-01-01

    Cholinergic innervation regulates the physiological and biochemical properties of skeletal muscle. The mechanisms that appear to be involved in this regulation include soluble, neurally-derived polypeptides, transmitter-evoked muscle activity and the neurotransmitter, acetylcholine, itself. Despite extensive research, the interacting neural mechanisms that control such macromolecules as acetylcholinesterase, the acetylcholine receptor and glucose 6-phosphate dehydrogenase remain unclear. It may be that more simplified in vitro model systems coupled with recent dramatic advances in the molecular biology of neurally-regulated proteins will begin to allow researchers to unravel the mechanisms controlling the expression and maintenance of these macromolecules.

  17. Neural Architectures for Control

    NASA Technical Reports Server (NTRS)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  18. Active control of the nucleation temperature enhances freezing survival of multipotent mesenchymal stromal cells.

    PubMed

    Lauterboeck, L; Hofmann, N; Mueller, T; Glasmacher, B

    2015-12-01

    Cryopreservation is a technique that has been extensively used for storage of multipotent mesenchymal stromal cells (MSCs) in regenerative medicine. Therefore, improving current cryopreservation procedures in terms of increasing cell viability and functionality is important. In this study, we optimized the cryopreservation protocol of MSCs derived from the common marmoset Callithrix jacchus (cj), which can be used as a non-human primate model in various pathological and transplantation studies and have a great potential for regenerative medicine. We have investigated the effect of the active control of the nucleation temperature using induced nucleation at a broad range of temperatures and two different dimethylsulfoxide concentrations (Me2SO, 5% (v/v) and 10%, (v/v)) to evaluate the overall effect on the viability, metabolic activity and recovery of cells after thawing. Survival rate and metabolic activity displayed an optimum when ice formation was induced at -10 °C. Cryomicroscopy studies indicated differences in ice crystal morphologies as well as differences in intracellular ice formation with different nucleation temperatures. High subzero nucleation temperatures resulted in larger extracellular ice crystals and cellular dehydration, whereas low subzero nucleation temperatures resulted in smaller ice crystals and intracellular ice formation. PMID:26499840

  19. Chaotic neural control

    NASA Astrophysics Data System (ADS)

    Potapov, A.; Ali, M. K.

    2001-04-01

    We consider the problem of stabilizing unstable equilibria by discrete controls (the controls take discrete values at discrete moments of time). We prove that discrete control typically creates a chaotic attractor in the vicinity of an equilibrium. Artificial neural networks with reinforcement learning are known to be able to learn such a control scheme. We consider examples of such systems, discuss some details of implementing the reinforcement learning to controlling unstable equilibria, and show that the arising dynamics is characterized by positive Lyapunov exponents, and hence is chaotic. This chaos can be observed both in the controlled system and in the activity patterns of the controller.

  20. Neural networks for aircraft control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  1. Neural Flight Control System

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen

    2003-01-01

    The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

  2. Wnt1 and BMP2: two factors recruiting multipotent neural crest progenitors isolated from adult bone marrow.

    PubMed

    Glejzer, A; Laudet, E; Leprince, P; Hennuy, B; Poulet, C; Shakhova, O; Sommer, L; Rogister, B; Wislet-Gendebien, S

    2011-06-01

    Recent studies have shown that neural crest-derived progenitor cells can be found in diverse mammalian tissues including tissues that were not previously shown to contain neural crest derivatives, such as bone marrow. The identification of those "new" neural crest-derived progenitor cells opens new strategies for developing autologous cell replacement therapies in regenerative medicine. However, their potential use is still a challenge as only few neural crest-derived progenitor cells were found in those new accessible locations. In this study, we developed a protocol, based on wnt1 and BMP2 effects, to enrich neural crest-derived cells from adult bone marrow. Those two factors are known to maintain and stimulate the proliferation of embryonic neural crest stem cells, however, their effects have never been characterized on neural crest cells isolated from adult tissues. Using multiple strategies from microarray to 2D-DIGE proteomic analyses, we characterized those recruited neural crest-derived cells, defining their identity and their differentiating abilities. PMID:20976520

  3. Neural Networks for Flight Control

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1996-01-01

    Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.

  4. Neural Control of the Circulation

    ERIC Educational Resources Information Center

    Thomas, Gail D.

    2011-01-01

    The purpose of this brief review is to highlight key concepts about the neural control of the circulation that graduate and medical students should be expected to incorporate into their general knowledge of human physiology. The focus is largely on the sympathetic nerves, which have a dominant role in cardiovascular control due to their effects to…

  5. Fabrication of multipotent poly-para-xylylene particles in controlled nanoscopic dimensions.

    PubMed

    Yuan, Ruei-Hung; Li, Yi-Jye; Sun, Ho-Yi; Wu, Chih-Yu; Guan, Zhen-Yu; Ho, Hsin-Ying; Fang, Cheng-Yuan; Chen, Hsien-Yeh

    2016-03-01

    In this study, poly-para-xylylene-based multifunctional nanoparticles (PPX-NPs) were fabricated. Based on the solubility characteristics determined for asymmetrically substituted poly-para-xylylenes in polar solvents, well-dispersed nanocolloids with a controllable size ranging from 50 to 800nm were produced in solution by the displacement of the solvent (water). These size ranges were found to have acceptable cellular compatibility through examinations of cultured 3T3 fibroblasts and adipose-derived stem cells treated with the PPX-NPs. In addition, these nanoscale PPX-NPs exhibited versatile bioconjugation properties in that a variety of available functional groups can be adopted from their counterpart, thin-film poly-para-xylylenes, during the production of these nanoparticles. For instance, bifunctional PPX-NPs with maleimide and benzoyl moieties were produced to enable immobilization via a maleimide-thiol reaction concurrent with a photochemical reaction. A cleavable PPX-NP was also produced with a thiol-exchangeable surface property. Additionally, by performing electrohydrodynamic jetting of parallel polymer solutions of selected poly-para-xylylenes, Janus-type or multicompartment PPX-NPs were created. The PPX-NPs can potentially be used for various biomedical applications such as combined diagnostics and drug delivery, multiplexing of detection, multiple-drug loading, and the targeted delivery of biomolecules or drugs. PMID:26724467

  6. Pigment Cell Progenitors in Zebrafish Remain Multipotent through Metamorphosis.

    PubMed

    Singh, Ajeet Pratap; Dinwiddie, April; Mahalwar, Prateek; Schach, Ursula; Linker, Claudia; Irion, Uwe; Nüsslein-Volhard, Christiane

    2016-08-01

    The neural crest is a transient, multipotent embryonic cell population in vertebrates giving rise to diverse cell types in adults via intermediate progenitors. The in vivo cell-fate potential and lineage segregation of these postembryonic progenitors is poorly understood, and it is unknown if and when the progenitors become fate restricted. We investigate the fate restriction in the neural crest-derived stem cells and intermediate progenitors in zebrafish, which give rise to three distinct adult pigment cell types: melanophores, iridophores, and xanthophores. By inducing clones in sox10-expressing cells, we trace and quantitatively compare the pigment cell progenitors at four stages, from embryogenesis to metamorphosis. At all stages, a large fraction of the progenitors are multipotent. These multipotent progenitors have a high proliferation ability, which diminishes with fate restriction. We suggest that multipotency of the nerve-associated progenitors lasting into metamorphosis may have facilitated the evolution of adult-specific traits in vertebrates. PMID:27453500

  7. Adaptive optimization and control using neural networks

    SciTech Connect

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  8. Control of cell-fate plasticity and maintenance of multipotency by DAF-16/FoxO in quiescent Caenorhabditis elegans.

    PubMed

    Karp, Xantha; Greenwald, Iva

    2013-02-01

    The Caenorhabditis elegans vulval precursor cells (VPCs) offer a paradigm for investigating how multipotency of progenitor cells is maintained during periods of quiescence. The VPCs are born in the first larval stage. When hermaphrodites are grown under favorable conditions, the EGF-mediated "inductive" signal and the LIN-12/Notch-mediated "lateral" signal confer a precise spatial pattern of distinct vulval cell fates in the third larval stage, a day after hatching. Under adverse conditions, hermaphrodites undergo a prolonged quiescent period as dauer larvae, which can endure for several months with progenitor cells such as VPCs in developmental arrest. If favorable conditions ensue, larvae recover and resume development as postdauer third stage larvae, with the same VPC spatial-patterning events as in continuously developing third stage larvae. Here, we identify several consequences of dauer life history for VPC specification. In wild-type dauers, VPCs undergo a phenomenon reminiscent of natural direct reprogramming to maintain or reestablish multipotency; they acquire an active block to signal transduction by EGF receptor and LIN-12/Notch and have a different mechanism for regulating transcription of the lateral signal. Furthermore, DAF-16/FoxO, a target of insulin/insulin-like growth factor signaling, is required to promote VPC fate plasticity during dauer and for normal vulval patterning after passage through dauer, suggesting that DAF-16/FoxO coordinates environment and life history with plasticity of cell fate. PMID:23341633

  9. Flexible body control using neural networks

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1992-01-01

    Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

  10. Neural Networks in Nonlinear Aircraft Control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis J.

    1990-01-01

    Recent research indicates that artificial neural networks offer interesting learning or adaptive capabilities. The current research focuses on the potential for application of neural networks in a nonlinear aircraft control law. The current work has been to determine which networks are suitable for such an application and how they will fit into a nonlinear control law.

  11. A conserved germline multipotency program

    PubMed Central

    Juliano, Celina E.; Swartz, S. Zachary; Wessel, Gary M.

    2010-01-01

    The germline of multicellular animals is segregated from somatic tissues, which is an essential developmental process for the next generation. Although certain ecdysozoans and chordates segregate their germline during embryogenesis, animals from other taxa segregate their germline after embryogenesis from multipotent progenitor cells. An overlapping set of genes, including vasa, nanos and piwi, operate in both multipotent precursors and in the germline. As we propose here, this conservation implies the existence of an underlying germline multipotency program in these cell types that has a previously underappreciated and conserved function in maintaining multipotency. PMID:21098563

  12. Advanced telerobotic control using neural networks

    NASA Technical Reports Server (NTRS)

    Pap, Robert M.; Atkins, Mark; Cox, Chadwick; Glover, Charles; Kissel, Ralph; Saeks, Richard

    1993-01-01

    Accurate Automation is designing and developing adaptive decentralized joint controllers using neural networks. We are then implementing these in hardware for the Marshall Space Flight Center PFMA as well as to be usable for the Remote Manipulator System (RMS) robot arm. Our design is being realized in hardware after completion of the software simulation. This is implemented using a Functional-Link neural network.

  13. Neural control of aggression in Drosophila.

    PubMed

    Hoopfer, Eric D

    2016-06-01

    Like most animal species, fruit flies fight to obtain and defend resources essential to survival and reproduction. Aggressive behavior in Drosophila is genetically specified and also strongly influenced by the fly's social context, past experiences and internal states, making it an excellent framework for investigating the neural mechanisms that regulate complex social behaviors. Here, I summarize our current knowledge of the neural control of aggression in Drosophila and discuss recent advances in understanding the sensory pathways that influence the decision to fight or court, the neuromodulatory control of aggression, the neural basis by which internal states can influence both fighting and courtship, and how social experience modifies aggressive behavior. PMID:27179788

  14. Neural-Network Controller For Vibration Suppression

    NASA Technical Reports Server (NTRS)

    Boussalis, Dhemetrios; Wang, Shyh Jong

    1995-01-01

    Neural-network-based adaptive-control system proposed for vibration suppression of flexible space structures. Controller features three-layer neural network and utilizes output feedback. Measurements generated by various sensors on structure. Feed forward path also included to speed up response in case plant exhibits predominantly linear dynamic behavior. System applicable to single-input single-output systems. Work extended to multiple-input multiple-output systems as well.

  15. Evolution of neural controllers for salamanderlike locomotion

    NASA Astrophysics Data System (ADS)

    Ijspeert, Auke J.

    1999-08-01

    This paper presents an experiment in which evolutionary algorithms are used for the development of neural controllers for salamander locomotion. The aim of the experiment is to investigate which kind of neural circuitry can produce the typical swimming and trotting gaits of the salamander, and to develop a synthetic approach to neurobiology by using genetic algorithms as design tool. A 2D bio-mechanical simulation of the salamander's body is developed whose muscle contraction is determined by the locomotion controller simulated as continuous-time neural networks. While the connectivity of the neural circuitry underlying locomotion in the salamander has not been decoded for the moment, the general organization of the designed neural circuits corresponds to that hypothesized by neurobiologist for the real animal. In particular, the locomotion controllers are based on a body central pattern generator (CPG) corresponding to a lamprey-like swimming controller as developed by Ekeberg, and are extended with a limb CPG for controlling the salamander's body. A genetic algorithm is used to instantiate synaptic weights of the connections within the limb CPG and from the limb CPG to the body CPG given a high level description of the desired gaits. A set of biologically plausible controllers are thus developed which can produce a neural activity and locomotion gaits very similar to those observed in the real salamander. By simply varying the external excitation applied to the network, the speed, direction and type of gait can be varied.

  16. Compliance control with embedded neural elements

    NASA Technical Reports Server (NTRS)

    Venkataraman, S. T.; Gulati, S.

    1992-01-01

    The authors discuss a control approach that embeds the neural elements within a model-based compliant control architecture for robotic tasks that involve contact with unstructured environments. Compliance control experiments have been performed on actual robotics hardware to demonstrate the performance of contact control schemes with neural elements. System parameters were identified under the assumption that environment dynamics have a fixed nonlinear structure. A robotics research arm, placed in contact with a single degree-of-freedom electromechanical environment dynamics emulator, was commanded to move through a desired trajectory. The command was implemented by using a compliant control strategy.

  17. Neural control of the immune system

    PubMed Central

    Sundman, Eva

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have suggested that vagus nerve stimulation can improve symptoms in human rheumatoid arthritis. These discoveries have generated an increased interest in bioelectronic medicine, i.e., therapeutic delivery of electrical impulses that activate nerves to regulate immune system function. Here, we discuss the physiology and potential therapeutic implications of neural immune control. PMID:25039084

  18. Neural networks and orbit control in accelerators

    SciTech Connect

    Bozoki, E.; Friedman, A.

    1994-07-01

    An overview of the architecture, workings and training of Neural Networks is given. We stress the aspects which are important for the use of Neural Networks for orbit control in accelerators and storage rings, especially its ability to cope with the nonlinear behavior of the orbit response to `kicks` and the slow drift in the orbit response during long-term operation. Results obtained for the two NSLS storage rings with several network architectures and various training methods for each architecture are given.

  19. Twist1 Controls a Cell-Specification Switch Governing Cell Fate Decisions within the Cardiac Neural Crest

    PubMed Central

    Vincentz, Joshua W.; Firulli, Beth A.; Lin, Andrea; Spicer, Douglas B.; Howard, Marthe J.; Firulli, Anthony B.

    2013-01-01

    Neural crest cells are multipotent progenitor cells that can generate both ectodermal cell types, such as neurons, and mesodermal cell types, such as smooth muscle. The mechanisms controlling this cell fate choice are not known. The basic Helix-loop-Helix (bHLH) transcription factor Twist1 is expressed throughout the migratory and post-migratory cardiac neural crest. Twist1 ablation or mutation of the Twist-box causes differentiation of ectopic neuronal cells, which molecularly resemble sympathetic ganglia, in the cardiac outflow tract. Twist1 interacts with the pro-neural factor Sox10 via its Twist-box domain and binds to the Phox2b promoter to repress transcriptional activity. Mesodermal cardiac neural crest trans-differentiation into ectodermal sympathetic ganglia-like neurons is dependent upon Phox2b function. Ectopic Twist1 expression in neural crest precursors disrupts sympathetic neurogenesis. These data demonstrate that Twist1 functions in post-migratory neural crest cells to repress pro-neural factors and thereby regulate cell fate determination between ectodermal and mesodermal lineages. PMID:23555309

  20. Neural networks as a control methodology

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1990-01-01

    While conventional computers must be programmed in a logical fashion by a person who thoroughly understands the task to be performed, the motivation behind neural networks is to develop machines which can train themselves to perform tasks, using available information about desired system behavior and learning from experience. There are three goals of this fellowship program: (1) to evaluate various neural net methods and generate computer software to implement those deemed most promising on a personal computer equipped with Matlab; (2) to evaluate methods currently in the professional literature for system control using neural nets to choose those most applicable to control of flexible structures; and (3) to apply the control strategies chosen in (2) to a computer simulation of a test article, the Control Structures Interaction Suitcase Demonstrator, which is a portable system consisting of a small flexible beam driven by a torque motor and mounted on springs tuned to the first flexible mode of the beam. Results of each are discussed.

  1. Neural and fuzzy robotic hand control.

    PubMed

    Tascillo, A; Bourbakis, N

    1999-01-01

    An efficient first grasp for a wheelchair robotic arm-hand with pressure sensing is determined and presented. The grasp is learned by combining the advantages of neural networks and fuzzy logic into a hybrid control algorithm which learns from its tip and slip control experiences. Neurofuzzy modifications are outlined, and basic steps are demonstrated in preparation for physical implementation. Choice of object approach vector based on fuzzy tip and slip data and an expert supervisor, as well as training of a diagnostic neural tip and slip controller, are the focus of this work. PMID:18252342

  2. Integrated Neural Flight and Propulsion Control System

    NASA Technical Reports Server (NTRS)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  3. Neural predictive control for active buffet alleviation

    NASA Astrophysics Data System (ADS)

    Pado, Lawrence E.; Lichtenwalner, Peter F.; Liguore, Salvatore L.; Drouin, Donald

    1998-06-01

    The adaptive neural control of aeroelastic response (ANCAR) and the affordable loads and dynamics independent research and development (IRAD) programs at the Boeing Company jointly examined using neural network based active control technology for alleviating undesirable vibration and aeroelastic response in a scale model aircraft vertical tail. The potential benefits of adaptive control includes reducing aeroelastic response associated with buffet and atmospheric turbulence, increasing flutter margins, and reducing response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and thus loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Wind tunnel tests were undertaken on a rigid 15% scale aircraft in Boeing's mini-speed wind tunnel, which is used for testing at very low air speeds up to 80 mph. The model included a dynamically scaled flexible fail consisting of an aluminum spar with balsa wood cross sections with a hydraulically powered rudder. Neural predictive control was used to actuate the vertical tail rudder in response to strain gauge feedback to alleviate buffeting effects. First mode RMS strain reduction of 50% was achieved. The neural predictive control system was developed and implemented by the Boeing Company to provide an intelligent, adaptive control architecture for smart structures applications with automated synthesis, self-optimization, real-time adaptation, nonlinear control, and fault tolerance capabilities. It is designed to solve complex control problems though a process of automated synthesis, eliminating costly control design and surpassing it in many instances by accounting for real world non-linearities.

  4. Adaptive neural control of aeroelastic response

    NASA Astrophysics Data System (ADS)

    Lichtenwalner, Peter F.; Little, Gerald R.; Scott, Robert C.

    1996-05-01

    The Adaptive Neural Control of Aeroelastic Response (ANCAR) program is a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC) under a Memorandum of Agreement (MOA). The purpose of the MOA is to cooperatively develop the smart structure technologies necessary for alleviating undesirable vibration and aeroelastic response associated with highly flexible structures. Adaptive control can reduce aeroelastic response associated with buffet and atmospheric turbulence, it can increase flutter margins, and it may be able to reduce response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Phase I of the ANCAR program involved development and demonstration of a neural network-based semi-adaptive flutter suppression system which used a neural network for scheduling control laws as a function of Mach number and dynamic pressure. This controller was tested along with a robust fixed-gain control law in NASA's Transonic Dynamics Tunnel (TDT) utilizing the Benchmark Active Controls Testing (BACT) wing. During Phase II, a fully adaptive on-line learning neural network control system has been developed for flutter suppression which will be tested in 1996. This paper presents the results of Phase I testing as well as the development progress of Phase II.

  5. Neural Control of the Lower Urinary Tract

    PubMed Central

    de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

    2015-01-01

    This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

  6. Toward Real Time Neural Net Flight Controllers

    NASA Technical Reports Server (NTRS)

    Jorgensen, C. C.; Mah, R. W.; Ross, J.; Lu, Henry, Jr. (Technical Monitor)

    1994-01-01

    NASA Ames Research Center has an ongoing program in neural network control technology targeted toward real time flight demonstrations using a modified F-15 which permits direct inner loop control of actuators, rapid switching between alternative control designs, and substitutable processors. An important part of this program is the ACTIVE flight project which is examining the feasibility of using neural networks in the design, control, and system identification of new aircraft prototypes. This paper discusses two research applications initiated with this objective in mind: utilization of neural networks for wind tunnel aircraft model identification and rapid learning algorithms for on line reconfiguration and control. The first application involves the identification of aerodynamic flight characteristics from analysis of wind tunnel test data. This identification is important in the early stages of aircraft design because complete specification of control architecture's may not be possible even though concept models at varying scales are available for aerodynamic wind tunnel testing. Testing of this type is often a long and expensive process involving measurement of aircraft lift, drag, and moment of inertia at varying angles of attack and control surface configurations. This information in turn can be used in the design of the flight control systems by applying the derived lookup tables to generate piece wise linearized controllers. Thus, reduced costs in tunnel test times and the rapid transfer of wind tunnel insights into prototype controllers becomes an important factor in more efficient generation and testing of new flight systems. NASA Ames Research Center is successfully applying modular neural networks as one way of anticipating small scale aircraft model performances prior to testing, thus reducing the number of in tunnel test hours and potentially, the number of intermediate scaled models required for estimation of surface flow effects.

  7. Seismic active control by neural networks.

    SciTech Connect

    Tang, Y.

    1998-01-01

    A study on the application of artificial neural networks (ANNs) to activate structural control under seismic loads is carried out. The structure considered is a single-degree-of-freedom (SDF) system with an active bracing device. The control force is computed by a trained neural network. The feed-forward neural network architecture and an adaptive back-propagation training algorithm is used in the study. The neural net is trained to reproduce the function that represents the response-excitation relationship of the SDF system under seismic loads. The input-output training patterns are generated randomly. In the back-propagation training algorithm, the learning rate is determined by ensuring the decrease of the error function at each epoch. The computer program implemented is validated by solving the classification of the XOR problem. Then, the trained ANN is used to compute the control force according to the control strategy. If the control force exceeds the actuator's capacity limit, it is set equal to that limit. The concept of the control strategy employed herein is to apply the control force at every time step to cancel the system velocity induced at the preceding time step so that the gradual rhythmic buildup of the response is destroyed. The ground motions considered in the numerical example are the 1940 El Centro earthquake and the 1979 Imperial Valley earthquake in California. The system responses with and without the control are calculated and compared. The feasibility and potential of applying ANNs to seismic active control is asserted by the promising results obtained from the numerical examples studied.

  8. Controlling neural network responsiveness: tradeoffs and constraints

    PubMed Central

    Keren, Hanna; Marom, Shimon

    2014-01-01

    In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole brain dynamics. Here, we present effective control of response features (probability and latency) of cortical networks in vitro over many hours, and offer this approach as an experimental toy for studying controllability of neural networks in the wider context. Exercising this approach we show that enforcement of stable high activity rates by means of closed loop control may enhance alteration of underlying global input–output relations and activity dependent dispersion of neuronal pair-wise correlations across the network. PMID:24808860

  9. Neural Control of Energy Expenditure.

    PubMed

    Münzberg, Heike; Qualls-Creekmore, Emily; Berthoud, Hans-Rudolf; Morrison, Christopher D; Yu, Sangho

    2016-01-01

    The continuous rise in obesity is a major concern for future healthcare management. Many strategies to control body weight focus on a permanent modification of food intake with limited success in the long term. Metabolism or energy expenditure is the other side of the coin for the regulation of body weight, and strategies to enhance energy expenditure are a current focus for obesity treatment, especially since the (re)-discovery of the energy depleting brown adipose tissue in adult humans. Conversely, several human illnesses like neurodegenerative diseases, cancer, or autoimmune deficiency syndrome suffer from increased energy expenditure and severe weight loss. Thus, strategies to modulate energy expenditure to target weight gain or loss would improve life expectancies and quality of life in many human patients. The aim of this book chapter is to give an overview of our current understanding and recent progress in energy expenditure control with specific emphasis on central control mechanisms. PMID:26578523

  10. Neural Control of Energy Expenditure

    PubMed Central

    Qualls-Creekmore, Emily; Berthoud, Hans-Rudolf; Morrison, Christopher D.; Yu, Sangho

    2016-01-01

    The continuous rise in obesity is a major concern for future healthcare management. Many strategies to control body weight focus on a permanent modification of food intake with limited success in the long term. Metabolism or energy expenditure is the other side of the coin for the regulation of body weight, and strategies to enhance energy expenditure are a current focus for obesity treatment, especially since the (re)-discovery of the energy depleting brown adipose tissue in adult humans. Conversely, several human illnesses like neurodegenerative diseases, cancer, or autoimmune deficiency syndrome suffer from increased energy expenditure and severe weight loss. Thus, strategies to modulate energy expenditure to target weight gain or loss would improve life expectancies and quality of life in many human patients. The aim of this book chapter is to give an overview of our current understanding and recent progress in energy expenditure control with specific emphasis on central control mechanisms. PMID:26578523

  11. Color control of printers by neural networks

    NASA Astrophysics Data System (ADS)

    Tominaga, Shoji

    1998-07-01

    A method is proposed for solving the mapping problem from the 3D color space to the 4D CMYK space of printer ink signals by means of a neural network. The CIE-L*a*b* color system is used as the device-independent color space. The color reproduction problem is considered as the problem of controlling an unknown static system with four inputs and three outputs. A controller determines the CMYK signals necessary to produce the desired L*a*b* values with a given printer. Our solution method for this control problem is based on a two-phase procedure which eliminates the need for UCR and GCR. The first phase determines a neural network as a model of the given printer, and the second phase determines the combined neural network system by combining the printer model and the controller in such a way that it represents an identity mapping in the L*a*b* color space. Then the network of the controller part realizes the mapping from the L*a*b* space to the CMYK space. Practical algorithms are presented in the form of multilayer feedforward networks. The feasibility of the proposed method is shown in experiments using a dye sublimation printer and an ink jet printer.

  12. The neural control of singing

    PubMed Central

    Zarate, Jean Mary

    2013-01-01

    Singing provides a unique opportunity to examine music performance—the musical instrument is contained wholly within the body, thus eliminating the need for creating artificial instruments or tasks in neuroimaging experiments. Here, more than two decades of voice and singing research will be reviewed to give an overview of the sensory-motor control of the singing voice, starting from the vocal tract and leading up to the brain regions involved in singing. Additionally, to demonstrate how sensory feedback is integrated with vocal motor control, recent functional magnetic resonance imaging (fMRI) research on somatosensory and auditory feedback processing during singing will be presented. The relationship between the brain and singing behavior will be explored also by examining: (1) neuroplasticity as a function of various lengths and types of training, (2) vocal amusia due to a compromised singing network, and (3) singing performance in individuals with congenital amusia. Finally, the auditory-motor control network for singing will be considered alongside dual-stream models of auditory processing in music and speech to refine both these theoretical models and the singing network itself. PMID:23761746

  13. Neural control: Chaos control sets the pace

    NASA Astrophysics Data System (ADS)

    Schöll, Eckehard

    2010-03-01

    Even simple creatures, such as cockroaches, are capable of complex responses to changes in their environment. But robots usually require complicated dedicated control circuits to perform just a single action. Chaos control theory could allow simpler control strategies to realize more complex behaviour.

  14. The neural optimal control hierarchy for motor control

    NASA Astrophysics Data System (ADS)

    DeWolf, T.; Eliasmith, C.

    2011-10-01

    Our empirical, neuroscientific understanding of biological motor systems has been rapidly growing in recent years. However, this understanding has not been systematically mapped to a quantitative characterization of motor control based in control theory. Here, we attempt to bridge this gap by describing the neural optimal control hierarchy (NOCH), which can serve as a foundation for biologically plausible models of neural motor control. The NOCH has been constructed by taking recent control theoretic models of motor control, analyzing the required processes, generating neurally plausible equivalent calculations and mapping them on to the neural structures that have been empirically identified to form the anatomical basis of motor control. We demonstrate the utility of the NOCH by constructing a simple model based on the identified principles and testing it in two ways. First, we perturb specific anatomical elements of the model and compare the resulting motor behavior with clinical data in which the corresponding area of the brain has been damaged. We show that damaging the assigned functions of the basal ganglia and cerebellum can cause the movement deficiencies seen in patients with Huntington's disease and cerebellar lesions. Second, we demonstrate that single spiking neuron data from our model's motor cortical areas explain major features of single-cell responses recorded from the same primate areas. We suggest that together these results show how NOCH-based models can be used to unify a broad range of data relevant to biological motor control in a quantitative, control theoretic framework.

  15. Multipotent adult progenitor cells on an allograft scaffold facilitate the bone repair process

    PubMed Central

    LoGuidice, Amanda; Houlihan, Alison; Deans, Robert

    2016-01-01

    Multipotent adult progenitor cells are a recently described population of stem cells derived from the bone marrow stroma. Research has demonstrated the potential of multipotent adult progenitor cells for treating ischemic injury and cardiovascular repair; however, understanding of multipotent adult progenitor cells in orthopedic applications remains limited. In this study, we evaluate the osteogenic and angiogenic capacity of multipotent adult progenitor cells, both in vitro and loaded onto demineralized bone matrix in vivo, with comparison to mesenchymal stem cells, as the current standard. When compared to mesenchymal stem cells, multipotent adult progenitor cells exhibited a more robust angiogenic protein release profile in vitro and developed more extensive vasculature within 2 weeks in vivo. The establishment of this vascular network is critical to the ossification process, as it allows nutrient exchange and provides an influx of osteoprogenitor cells to the wound site. In vitro assays confirmed the multipotency of multipotent adult progenitor cells along mesodermal lineages and demonstrated the enhanced expression of alkaline phosphatase and production of calcium-containing mineral deposits by multipotent adult progenitor cells, necessary precursors for osteogenesis. In combination with a demineralized bone matrix scaffold, multipotent adult progenitor cells demonstrated enhanced revascularization and new bone formation in vivo in an orthotopic defect model when compared to mesenchymal stem cells on demineralized bone matrix or demineralized bone matrix–only control groups. The potent combination of angiogenic and osteogenic properties provided by multipotent adult progenitor cells appears to create a synergistic amplification of the bone healing process. Our results indicate that multipotent adult progenitor cells have the potential to better promote tissue regeneration and healing and to be a functional cell source for use in orthopedic applications

  16. Multipotent adult progenitor cells on an allograft scaffold facilitate the bone repair process.

    PubMed

    LoGuidice, Amanda; Houlihan, Alison; Deans, Robert

    2016-01-01

    Multipotent adult progenitor cells are a recently described population of stem cells derived from the bone marrow stroma. Research has demonstrated the potential of multipotent adult progenitor cells for treating ischemic injury and cardiovascular repair; however, understanding of multipotent adult progenitor cells in orthopedic applications remains limited. In this study, we evaluate the osteogenic and angiogenic capacity of multipotent adult progenitor cells, both in vitro and loaded onto demineralized bone matrix in vivo, with comparison to mesenchymal stem cells, as the current standard. When compared to mesenchymal stem cells, multipotent adult progenitor cells exhibited a more robust angiogenic protein release profile in vitro and developed more extensive vasculature within 2 weeks in vivo. The establishment of this vascular network is critical to the ossification process, as it allows nutrient exchange and provides an influx of osteoprogenitor cells to the wound site. In vitro assays confirmed the multipotency of multipotent adult progenitor cells along mesodermal lineages and demonstrated the enhanced expression of alkaline phosphatase and production of calcium-containing mineral deposits by multipotent adult progenitor cells, necessary precursors for osteogenesis. In combination with a demineralized bone matrix scaffold, multipotent adult progenitor cells demonstrated enhanced revascularization and new bone formation in vivo in an orthotopic defect model when compared to mesenchymal stem cells on demineralized bone matrix or demineralized bone matrix-only control groups. The potent combination of angiogenic and osteogenic properties provided by multipotent adult progenitor cells appears to create a synergistic amplification of the bone healing process. Our results indicate that multipotent adult progenitor cells have the potential to better promote tissue regeneration and healing and to be a functional cell source for use in orthopedic applications. PMID

  17. Adaptive neural control of spacecraft using control moment gyros

    NASA Astrophysics Data System (ADS)

    Leeghim, Henzeh; Kim, Donghoon

    2015-03-01

    An adaptive control technique is applied to reorient spacecraft with uncertainty using control moment gyros. A nonlinear quaternion feedback law is chosen as a baseline controller. An additional adaptive control input supported by neural networks can estimate and eliminate unknown terms adaptively. The normalized input neural networks are considered for reliable computation of the adaptive input. To prove the stability of the closed-loop dynamics with the control law, the Lyapunov stability theory is considered. Accordingly, the proposed approach results in the uniform ultimate boundedness in tracking error. For reorientation maneuvers, control moment gyros are utilized with a well-known singularity problem described in this work investigated by predicting one-step ahead singularity index. A momentum vector recovery approach using magnetic torquers is also introduced to evaluate the avoidance strategies indirectly. Finally, the suggested methods are demonstrated by numerical simulation studies.

  18. Microturbine control based on fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Yan, Shijie; Bian, Chunyuan; Wang, Zhiqiang

    2006-11-01

    As microturbine generator (MTG) is a clean, efficient, low cost and reliable energy supply system. From outside characteristics of MTG, it is multi-variable, time-varying and coupling system, so it is difficult to be identified on-line and conventional control law adopted before cannot achieve desirable result. A novel fuzzy-neural networks (FNN) control algorithm was proposed in combining with the conventional PID control. In the paper, IF-THEN rules for tuning were applied by a first-order Sugeno fuzzy model with seven fuzzy rules and the membership function was given as the continuous GAUSSIAN function. Some sample data were utilized to train FNN. Through adjusting shape of membership function and weight continually, objective of auto-tuning fuzzy-rules can be achieved. The FNN algorithm had been applied to "100kW Microturbine control and power converter system". The results of simulation and experiment are shown that the algorithm can work very well.

  19. Towards practical control design using neural computation

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Garg, Sanjay; Mattern, Duane; Merrill, Walter

    1991-01-01

    The objective is to develop neural network based control design techniques which address the issue of performance/control effort tradeoff. Additionally, the control design needs to address the important issue if achieving adequate performance in the presence of actuator nonlinearities such as position and rate limits. These issues are discussed using the example of aircraft flight control. Given a set of pilot input commands, a feedforward net is trained to control the vehicle within the constraints imposed by the actuators. This is achieved by minimizing an objective function which is the sum of the tracking errors, control input rates and control input deflections. A tradeoff between tracking performance and control smoothness is obtained by varying, adaptively, the weights of the objective function. The neurocontroller performance is evaluated in the presence of actuator dynamics using a simulation of the vehicle. Appropriate selection of the different weights in the objective function resulted in the good tracking of the pilot commands and smooth neurocontrol. An extension of the neurocontroller design approach is proposed to enhance its practicality.

  20. Neural networks for LED color control

    NASA Astrophysics Data System (ADS)

    Ashdown, Ian E.

    2004-01-01

    The design and implementation of an architectural dimming control for multicolor LED-based lighting fixtures is complicated by the need to maintain a consistent color balance under a wide variety of operating conditions. Factors to consider include nonlinear relationships between luminous flux intensity and drive current, junction temperature dependencies, LED manufacturing tolerances and binning parameters, device aging characteristics, variations in color sensor spectral responsitivities, and the approximations introduced by linear color space models. In this paper we formulate this problem as a nonlinear multidimensional function, where maintaining a consistent color balance is equivalent to determining the hyperplane representing constant chromaticity. To be useful for an architectural dimming control design, this determination must be made in real time as the lighting fixture intensity is adjusted. Further, the LED drive current must be continuously adjusted in response to color sensor inputs to maintain constant chromaticity for a given intensity setting. Neural networks are known to be universal approximators capable of representing any continuously differentiable bounded function. We therefore use a radial basis function neural network to represent the multidimensional function and provide the feedback signals needed to maintain constant chromaticity. The network can be trained on the factory floor using individual device measurements such as spectral radiant intensity and color sensor characteristics. This provides a flexible solution that is mostly independent of LED manufacturing tolerances and binning parameters.

  1. Fuzzy-neural control of an aircraft tracking camera platform

    NASA Technical Reports Server (NTRS)

    Mcgrath, Dennis

    1994-01-01

    A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.

  2. MiR-133b promotes neural plasticity and functional recovery after treatment of stroke with multipotent mesenchymal stromal cells in rats via transfer of exosome-enriched extracellular particles

    PubMed Central

    Xin, Hongqi; Li, Yi; Liu, Zhongwu; Wang, Xinli; Shang, Xia; Cui, Yisheng; Zhang, Zheng Gang; Chopp, Michael

    2013-01-01

    To test, in vivo, the hypothesis that exosomes from multipotent mesenchymal stromal cells (MSCs) mediate microRNA 133b (miR-133b) transfer which promotes neurological recovery from stroke, we employed knock-in and knock-down technologies to up-regulate or down-regulate the miR-133b level in MSCs (miR-133b+MSCs or miR-133b−MSCs) and their corresponding exosomes, respectively. Rats were subjected to middle cerebral artery occlusion (MCAo) and were treated with naïve MSCs, miR-133b+MSCs, or miR-133b−MSC at one day after MCAo. Compared with controls, rats receiving naïve MSC treatment significantly improved functional recovery, and exhibited increased axonal plasticity and neurite remodeling in the ischemic boundary zone (IBZ) at day 14 after MCAo. The outcomes were significantly enhanced with miR-133b+MSC treatment, and were significantly decreased with miR-133b−MSC treatment, compared to naïve MSC treatment. The miR-133b level in exosomes collected from the cerebral spinal fluid was significantly increased after miR-133b+MSC treatment, and was significantly decreased after miR-133b−MSC treatment at day 14 after MCAo, compared to naïve MSC treatment. Tagging exosomes with green fluorescent protein demonstrated that exosomes-enriched extracellular particles were released from MSCs and transferred to adjacent astrocytes and neurons. The expression of selective targets for miR-133b, connective tissue growth factor and ras homolog gene family member A, were significantly decreased in the IBZ after miR-133b+MSC treatment, while their expression remained at similar elevated levels after miR-133b−MSC treatment, compared to naïve MSC treatment. Collectively, our data suggest that exosomes from MSCs mediate the miR-133b transfer to astrocytes and neurons, which regulate gene expression, subsequently benefit neurite remodeling and functional recovery after stroke. PMID:23630198

  3. Understanding the brain by controlling neural activity

    PubMed Central

    Krug, Kristine; Salzman, C. Daniel; Waddell, Scott

    2015-01-01

    Causal methods to interrogate brain function have been employed since the advent of modern neuroscience in the nineteenth century. Initially, randomly placed electrodes and stimulation of parts of the living brain were used to localize specific functions to these areas. Recent technical developments have rejuvenated this approach by providing more precise tools to dissect the neural circuits underlying behaviour, perception and cognition. Carefully controlled behavioural experiments have been combined with electrical devices, targeted genetically encoded tools and neurochemical approaches to manipulate information processing in the brain. The ability to control brain activity in these ways not only deepens our understanding of brain function but also provides new avenues for clinical intervention, particularly in conditions where brain processing has gone awry. PMID:26240417

  4. Are muscle synergies useful for neural control?

    PubMed

    de Rugy, Aymar; Loeb, Gerald E; Carroll, Timothy J

    2013-01-01

    The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an electromyograms (EMG)-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors (AEs) in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions). Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals), but with higher AEs. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behavior depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to restore

  5. Experimental results of a predictive neural network HVAC controller

    SciTech Connect

    Jeannette, E.; Assawamartbunlue, K.; Kreider, J.F.; Curtiss, P.S.

    1998-12-31

    Proportional, integral, and derivative (PID) control is widely used in many HVAC control processes and requires constant attention for optimal control. Artificial neural networks offer the potential for improved control of processes through predictive techniques. This paper introduces and shows experimental results of a predictive neural network (PNN) controller applied to an unstable hot water system in an air-handling unit. Actual laboratory testing of the PNN and PID controllers show favorable results for the PNN controller.

  6. Multi-layer neural networks for robot control

    NASA Technical Reports Server (NTRS)

    Pourboghrat, Farzad

    1989-01-01

    Two neural learning controller designs for manipulators are considered. The first design is based on a neural inverse-dynamics system. The second is the combination of the first one with a neural adaptive state feedback system. Both types of controllers enable the manipulator to perform any given task very well after a period of training and to do other untrained tasks satisfactorily. The second design also enables the manipulator to compensate for unpredictable perturbations.

  7. Control of neural chaos by synaptic noise.

    PubMed

    Cortes, J M; Torres, J J; Marro, J

    2007-02-01

    We study neural automata - or neurobiologically inspired cellular automata - which exhibits chaotic itinerancy among the different stored patterns or memories. This is a consequence of activity-dependent synaptic fluctuations, which continuously destabilize the attractor and induce irregular hopping to other possible attractors. The nature of these irregularities depends on the dynamic details, namely, on the intensity of the synaptic noise and the number of sites of the network, which are synchronously updated at each time step. Varying these factors, different regimes occur, ranging from regular to chaotic dynamics. As a result, and in absence of external agents, the chaotic behavior may turn regular after tuning the noise intensity. It is argued that a similar mechanism might be on the basis of self-controlling chaos in natural systems. PMID:17084962

  8. Real-time Adaptive Control Using Neural Generalized Predictive Control

    NASA Technical Reports Server (NTRS)

    Haley, Pam; Soloway, Don; Gold, Brian

    1999-01-01

    The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

  9. Neural joint control for Space Shuttle Remote Manipulator System

    NASA Technical Reports Server (NTRS)

    Atkins, Mark A.; Cox, Chadwick J.; Lothers, Michael D.; Pap, Robert M.; Thomas, Charles R.

    1992-01-01

    Neural networks are being used to control a robot arm in a telerobotic operation. The concept uses neural networks for both joint and inverse kinematics in a robotic control application. An upper level neural network is trained to learn inverse kinematic mappings. The output, a trajectory, is then fed to the Decentralized Adaptive Joint Controllers. This neural network implementation has shown that the controlled arm recovers from unexpected payload changes while following the reference trajectory. The neural network-based decentralized joint controller is faster, more robust and efficient than conventional approaches. Implementations of this architecture are discussed that would relax assumptions about dynamics, obstacles, and heavy loads. This system is being developed to use with the Space Shuttle Remote Manipulator System.

  10. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

    It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

  11. Accelerator diagnosis and control by Neural Nets

    SciTech Connect

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.

  12. Neural Networks for Signal Processing and Control

    NASA Astrophysics Data System (ADS)

    Hesselroth, Ted Daniel

    Neural networks are developed for controlling a robot-arm and camera system and for processing images. The networks are based upon computational schemes that may be found in the brain. In the first network, a neural map algorithm is employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm employed shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a set of pressures corresponding to the end effector positions, as well as a set of Jacobian matrices for interpolating between these positions. Because of the properties of the rubber-tube actuators of the arm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after two hundred learning steps. Applications of repeated corrections in each step via the Jacobian matrices leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that they yield a reduction of the distance between gripper and target. The second network is proposed as a model for the mammalian vision system in which backward connections from the primary visual cortex (V1) to the lateral geniculate nucleus play a key role. The application of hebbian learning to the forward and backward connections causes the formation of receptive fields which are sensitive to edges, bars, and spatial frequencies of preferred orientations. The receptive fields are learned in such a way as to maximize the rate of transfer of information from the LGN to V1. Orientational preferences are organized into a feature map in the primary visual

  13. Distributed memory approaches for robotic neural controllers

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1990-01-01

    The suitability is explored of two varieties of distributed memory neutral networks as trainable controllers for a simulated robotics task. The task requires that two cameras observe an arbitrary target point in space. Coordinates of the target on the camera image planes are passed to a neural controller which must learn to solve the inverse kinematics of a manipulator with one revolute and two prismatic joints. Two new network designs are evaluated. The first, radial basis sparse distributed memory (RBSDM), approximates functional mappings as sums of multivariate gaussians centered around previously learned patterns. The second network types involved variations of Adaptive Vector Quantizers or Self Organizing Maps. In these networks, random N dimensional points are given local connectivities. They are then exposed to training patterns and readjust their locations based on a nearest neighbor rule. Both approaches are tested based on their ability to interpolate manipulator joint coordinates for simulated arm movement while simultaneously performing stereo fusion of the camera data. Comparisons are made with classical k-nearest neighbor pattern recognition techniques.

  14. Neural Control of the Immune System

    ERIC Educational Resources Information Center

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  15. Control chart pattern recognition using a back propagation neural network

    NASA Astrophysics Data System (ADS)

    Spoerre, Julie K.; Perry, Marcus B.

    2000-10-01

    In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). On-line automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. A back propagation artificial neural network is used to model X-bar quality control charts and identify process instability situations as specified by the Western Electric Statistical Quality Control handbook. Results indicate that the performance of the back propagation neural network is very accurate in identifying these control chart patterns. This work is significant in that the neural network output can serve as a link to process parameters in a closed-loop control system. In this way, adjustments to the process can be made on-line and quality problems averted.

  16. Thermoelastic steam turbine rotor control based on neural network

    NASA Astrophysics Data System (ADS)

    Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

    2015-12-01

    Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

  17. Neural-Network Control Of Prosthetic And Robotic Hands

    NASA Technical Reports Server (NTRS)

    Buckley, Theresa M.

    1991-01-01

    Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

  18. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    SciTech Connect

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-06-12

    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.

  19. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    NASA Astrophysics Data System (ADS)

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-06-01

    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.

  20. Implementations of learning control systems using neural networks

    NASA Technical Reports Server (NTRS)

    Sartori, Michael A.; Antsaklis, Panos J.

    1992-01-01

    The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.

  1. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  2. Neural network based diagonal decoupling control of powered wheelchair systems.

    PubMed

    Nguyen, Tuan Nghia; Su, Steven; Nguyen, Hung T

    2014-03-01

    This paper proposes an advanced diagonal decoupling control method for powered wheelchair systems. This control method is based on a combination of the systematic diagonalization technique and the neural network control design. As such, this control method reduces coupling effects on a multivariable system, leading to independent control design procedures. Using an obtained dynamic model, the problem of the plant's Jacobian calculation is eliminated in a neural network control design. The effectiveness of the proposed control method is verified in a real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty effects. PMID:23981543

  3. Neural Networks for Modeling and Control of Particle Accelerators

    DOE PAGESBeta

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

  4. Multipotent progenitor cells isolated from adult human pancreatic tissue.

    PubMed

    Todorov, I; Nair, I; Ferreri, K; Rawson, J; Kuroda, A; Pascual, M; Omori, K; Valiente, L; Orr, C; Al-Abdullah, I; Riggs, A; Kandeel, F; Mullen, Y

    2005-10-01

    The supply of islet cells is a limiting factor for the widespread application of islet transplantation of type-1 diabetes. Islets constitute 1% to 2% of pancreatic tissue, leaving approximately 98% as discard after islet isolation and purification. In this report we present our data on the isolation of multipotent progenitor cells from discarded adult human pancreatic tissue. The collected cells from discarded nonislet fractions, after enzymatic digestion and gradient purification of islets, were dissociated for suspension culture in a serum-free medium. The cell clusters grown to a size of 100 to 150 mum contained cells staining for stage-specific embryonic antigens, but not insulin or C-peptide. To direct cell differentiation toward islets, clusters were recultured in a pancreatic differentiation medium. Insulin and C-peptide-positive cells by immunocytochemistry appeared within a week, reaching over 10% of the cell population. Glucagon and somatostatin-positive cells were also detected. The cell clusters were found to secrete insulin in response to glucose stimulation. Cells from the same clusters also had the capacity for differentiation into neural cells, as documented by staining for neural and glial cell markers when cultured as monolayers in media containing neurotrophic factors. These data suggest that multipotent pancreatic progenitor cells exist within the human pancreatic tissue that is typically discarded during islet isolation procedures. These adult progenitor cells can be successfully differentiated into insulin-producing cells, and thus they have the potential for treatment of type-1 diabetes mellitus. PMID:16298614

  5. Adaptive artificial neural network for autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  6. Recognition with self-control in neural networks

    NASA Astrophysics Data System (ADS)

    Lewenstein, Maciej; Nowak, Andrzej

    1989-10-01

    We present a theory of fully connected neural networks that incorporates mechanisms of dynamical self-control of recognition process. Using a functional integral technique, we formulate mean-field dynamics for such systems.

  7. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method. PMID:26285223

  8. Neural control of helicopter blade-vortex interaction noise

    NASA Astrophysics Data System (ADS)

    Glaessel, Holger; Kloeppel, Valentin; Rudolph, Stephan

    2001-06-01

    Significant reduction of helicopter blade-vortex interaction (BVI) noise is currently one of the most advanced research topics in the helicopter industry. This is due to the complex flow, the close aerodynamic and structural coupling, and the interaction of the blades with the trailing edge vortices. Analytical and numerical modeling techniques are therefore currently still far from a sufficient degree of accuracy to obtain satisfactory results using classical model based control concepts. Neural networks with a proven potential to learn nonlinear relationships implicitly encoded in a training data set are therefore an appropriate and complementary technique for the alternative design of a nonlinear controller for BVI noise reduction. For nonlinear and adaptive control different neural control strategies have been proposed. Two possible approaches, a direct and an indirect neural controller are described. In indirect neural control, the plant has to be identified first by training a network with measured data. The plant network is then used to train the controller network. On the other hand the direct control approach does not rely on an explicit plant model, instead a specific training algorithm (like reinforcement learning) uses the information gathered from interactions with the environment. In the investigation of the BVI noise phenomena, helicopter developers have undertaken substantial efforts in full scale flight tests and wind tunnel experiments. Data obtained in these experiments have been adequately preprocessed using wavelet analysis and filtering techniques and are then used in the design of a neural controller. Neural open-loop control and neural closed-loop control concepts for the BVI noise reduction problem are conceived, simulated and compared against each other in this work in the above mentioned framework.

  9. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    USGS Publications Warehouse

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  10. Factors controlling cardiac neural crest cell migration

    PubMed Central

    Hutson, Mary R

    2010-01-01

    Cardiac neural crest cells originate as part of the postotic caudal rhombencephalic neural crest stream. Ectomesenchymal cells in this stream migrate to the circumpharyngeal ridge and then into the caudal pharyngeal arches where they condense to form first a sheath and then the smooth muscle tunics of the persisting pharyngeal arch arteries. A subset of the cells continues migrating into the cardiac outflow tract where they will condense to form the aorticopulmonary septum. Cell signaling, extracellular matrix and cell-cell contacts are all critical for the initial migration, pauses, continued migration and condensation of these cells. This Review elucidates what is currently known about these factors. PMID:20890117

  11. Adaptive control of nonlinear systems using multistage dynamic neural networks

    NASA Astrophysics Data System (ADS)

    Gupta, Madan M.; Rao, Dandina H.

    1992-11-01

    In this paper we present a new architecture of neuron, called the dynamic neural unit (DNU). The topology of the proposed neuronal model embodies delay elements, feedforward and feedback signals weighted by the synaptic weights and a time-varying nonlinear activation function, and is thus different from the conventionally and assumed architecture of neurons. The learning algorithm for the proposed neuronal structure and the corresponding implementation scheme are presented. A multi-stage dynamic neural network is developed using the DNU as the basic processing element. The performance evaluation of the dynamic neural network is presented for nonlinear dynamic systems under various situations. The capabilities of the proposed neural network model not only account for the learning and control actions emulating some of the biological control functions, but also provide a promising parallel-distributed intelligent control scheme for large-scale complex dynamic systems.

  12. Distributed neural control of a hexapod walking vehicle

    NASA Technical Reports Server (NTRS)

    Beer, R. D.; Sterling, L. S.; Quinn, R. D.; Chiel, H. J.; Ritzmann, R.

    1989-01-01

    There has been a long standing interest in the design of controllers for multilegged vehicles. The approach is to apply distributed control to this problem, rather than using parallel computing of a centralized algorithm. Researchers describe a distributed neural network controller for hexapod locomotion which is based on the neural control of locomotion in insects. The model considers the simplified kinematics with two degrees of freedom per leg, but the model includes the static stability constraint. Through simulation, it is demonstrated that this controller can generate a continuous range of statically stable gaits at different speeds by varying a single control parameter. In addition, the controller is extremely robust, and can continue the function even after several of its elements have been disabled. Researchers are building a small hexapod robot whose locomotion will be controlled by this network. Researchers intend to extend their model to the dynamic control of legs with more than two degrees of freedom by using data on the control of multisegmented insect legs. Another immediate application of this neural control approach is also exhibited in biology: the escape reflex. Advanced robots are being equipped with tactile sensing and machine vision so that the sensory inputs to the robot controller are vast and complex. Neural networks are ideal for a lower level safety reflex controller because of their extremely fast response time. The combination of robotics, computer modeling, and neurobiology has been remarkably fruitful, and is likely to lead to deeper insights into the problems of real time sensorimotor control.

  13. Nonlinear control structures based on embedded neural system models.

    PubMed

    Lightbody, G; Irwin, G W

    1997-01-01

    This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper. PMID:18255659

  14. Variable neural adaptive robust control: a switched system approach.

    PubMed

    Lian, Jianming; Hu, Jianghai; Żak, Stanislaw H

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multiinput multioutput uncertain systems. The controllers incorporate a novel variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. It can determine the network structure online dynamically by adding or removing RBFs according to the tracking performance. The structure variation is systematically considered in the stability analysis of the closed-loop system using a switched system approach with the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations. PMID:25881366

  15. Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

    SciTech Connect

    Kmet', Tibor; Kmet'ova, Maria

    2009-09-09

    A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  16. Neural controller for adaptive movements with unforeseen payloads

    NASA Technical Reports Server (NTRS)

    Kuperstein, Michael; Wang, Jyhpyng

    1990-01-01

    A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3 percent of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

  17. Neural Network Control of a Magnetically Suspended Rotor System

    NASA Technical Reports Server (NTRS)

    Choi, Benjamin; Brown, Gerald; Johnson, Dexter

    1997-01-01

    Abstract Magnetic bearings offer significant advantages because of their noncontact operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. This paper discusses the use of a neural network as a nonlinear controller that circumvents system nonlinearity. A neural network controller was well trained and successfully demonstrated on a small magnetic bearing rig. This work demonstrated the feasibility of using a neural network to control nonlinear magnetic bearings and systems with unknown dynamics.

  18. Neural Control of Energy Balance: Translating Circuits to Therapies

    PubMed Central

    Gautron, Laurent; Elmquist, Joel K.; Williams, Kevin W.

    2015-01-01

    Recent insights into the neural circuits controlling energy balance and glucose homeostasis have rekindled the hope for development of novel treatments for obesity and diabetes. However, many therapies contribute relatively modest beneficial gains with accompanying side effects, and the mechanisms of action for other interventions remain undefined. This Review summarizes current knowledge linking the neural circuits regulating energy and glucose balance with current and potential pharmacotherapeutic and surgical interventions for the treatment of obesity and diabetes. PMID:25815991

  19. Establishment of oct4:gfp transgenic zebrafish line for monitoring cellular multipotency by GFP fluorescence.

    PubMed

    Kato, Hiroyuki; Abe, Kota; Yokota, Shinpei; Matsuno, Rinta; Mikekado, Tsuyoshi; Yokoi, Hayato; Suzuki, Tohru

    2015-01-01

    The establishment of induced pluripotent stem (iPS) cell technology in fish could facilitate the establishment of novel cryopreservation techniques for storing selected aquaculture strains as frozen cells. In order to apply iPS cell technology to fish, we established a transgenic zebrafish line, Tg(Tru.oct4:EGFP), using green fluorescent protein (GFP) expression under the control of the oct4 gene promoter as a marker to evaluate multipotency in iPS cell preparations. We used the oct4 promoter from fugu (Takifugu rubripes) due to the compact nature of the fugu genome and to facilitate future applications of this technology in marine fishes. During embryogenesis, maternal GFP fluorescence was observed at the cleavage stage and zygotic GFP expression was observed from the start of the shield stage until approximately 24 h after fertilization. gfp messenger RNA (mRNA) was expressed by whole embryonic cells at the shield stage, and then restricted to the caudal neural tube in the latter stages of embryogenesis. These observations showed that GFP fluorescence and the regulation of gfp mRNA expression by the exogenous fugu oct4 promoter are well suited for monitoring endogenous oct4 mRNA expression in embryos. Bisulfite sequencing revealed that the rate of CpG methylation in the transgenic oct4 promoter was high in adult cells (98%) and low in embryonic cells (37%). These findings suggest that, as with the endogenous oct4 promoter, demethylation and methylation both take place normally in the transgenic oct4 promoter during embryogenesis. The embryonic cells harvested at the shield stage formed embryonic body-like cellular aggregates and maintained GFP fluorescence for 6 d when cultured on Transwell-COL Permeable Supports or a feeder layer of adult fin cells. Loss of GFP fluorescence by cultured cells was correlated with cellular differentiation. We consider that the Tg(Tru.oct4:EGFP) zebrafish line established here is well suited for monitoring multipotency in

  20. Integrated evolutionary computation neural network quality controller for automated systems

    SciTech Connect

    Patro, S.; Kolarik, W.J.

    1999-06-01

    With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

  1. An artificial neural network controller for intelligent transportation systems applications

    SciTech Connect

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J.

    1996-04-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  2. On neural networks in identification and control of dynamic systems

    NASA Technical Reports Server (NTRS)

    Phan, Minh; Juang, Jer-Nan; Hyland, David C.

    1993-01-01

    This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

  3. A neural network controller for automated composite manufacturing

    NASA Technical Reports Server (NTRS)

    Lichtenwalner, Peter F.

    1994-01-01

    At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

  4. Proliferation control in neural stem and progenitor cells

    PubMed Central

    Homem, Catarina CF; Repic, Marko; Knoblich, Juergen A

    2015-01-01

    Neural circuit function can be drastically affected by variations in the number of cells that are produced during development or by a reduction in adult cell number due to disease. Unlike many other organs, the brain is unable to compensate for such changes by increasing cell numbers or altering the size of the cells. For this reason, unique cell cycle and cell growth control mechanisms operate in the developing and adult brain. In Drosophila melanogaster and mammalian neural stem and progenitor cells these mechanisms are intricately coordinated with the developmental age and the nutritional, metabolic and hormonal state of the animal. Defects in neural stem cell proliferation that result in the generation of incorrect cell numbers or defects in neural stem cell differentiation can cause microcephaly or megalencephaly. PMID:26420377

  5. Neural network guided search control in partial order planning

    SciTech Connect

    Zimmerman, T.

    1996-12-31

    The development of efficient search control methods is an active research topic in the field of planning. Investigation of a planning program integrated with a neural network (NN) that assists in search control is underway, and has produced promising preliminary results.

  6. Application of neural networks to seismic active control

    SciTech Connect

    Tang, Yu

    1995-07-01

    An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads.

  7. Steam turbine stress control using NARX neural network

    NASA Astrophysics Data System (ADS)

    Dominiczak, Krzysztof; Rzadkowski, Romuald; Radulski, Wojciech

    2015-11-01

    Considered here is concept of steam turbine stress control, which is based on Nonlinear AutoRegressive neural networks with eXogenous inputs. Using NARX neural networks,whichwere trained based on experimentally validated FE model allows to control stresses in protected thickwalled steam turbine element with FE model quality. Additionally NARX neural network, which were trained base on FE model, includes: nonlinearity of steam expansion in turbine steam path during transients, nonlinearity of heat exchange inside the turbine during transients and nonlinearity of material properties during transients. In this article NARX neural networks stress controls is shown as an example of HP rotor of 18K390 turbine. HP part thermodynamic model as well as heat exchange model in vicinity of HP rotor,whichwere used in FE model of the HP rotor and the HP rotor FE model itself were validated based on experimental data for real turbine transient events. In such a way it is ensured that NARX neural network behave as real HP rotor during steam turbine transient events.

  8. Neural control of phrenic motoneuron discharge

    PubMed Central

    Lee, Kun-Ze; Fuller, David D.

    2011-01-01

    Phrenic motoneurons (PMNs) provide a synaptic relay between bulbospinal respiratory pathways and the diaphragm muscle. PMNs also receive propriospinal inputs, although the functional role of these interneuronal projections has not been established. Here we review the literature regarding PMN discharge patterns during breathing and the potential mechanisms that underlie PMN recruitment. Anatomical and neurophysiological studies indicate that PMNs form a heterogeneous pool, with respiratory-related PMN discharge and recruitment patterns likely determined by a balance between intrinsic MN properties and extrinsic synaptic inputs. We also review the limited literature regarding PMN bursting during respiratory plasticity. Differential recruitment or rate modulation of PMN subtypes may underlie phrenic motor plasticity following neural injury and/or respiratory stimulation; however this possibility remains relatively unexplored. PMID:21376841

  9. DC motor speed control using neural networks

    NASA Astrophysics Data System (ADS)

    Tai, Heng-Ming; Wang, Junli; Kaveh, Ashenayi

    1990-08-01

    This paper presents a scheme that uses a feedforward neural network for the learning and generalization of the dynamic characteristics for the starting of a dc motor. The goal is to build an intelligent motor starter which has a versatility equivalent to that possessed by a human operator. To attain a fast and safe starting from stall for a dc motor a maximum armature current should be maintained during the starting period. This can be achieved by properly adjusting the armature voltage. The network is trained to learn the inverse dynamics of the motor starting characteristics and outputs a proper armature voltage. Simulation was performed to demonstrate the feasibility and effectiveness of the model. This study also addresses the network performance as a function of the number of hidden units and the number of training samples. 1.

  10. Neural network application to aircraft control system design

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  11. Neural network application to aircraft control system design

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

  12. Dissipative rendering and neural network control system design

    NASA Technical Reports Server (NTRS)

    Gonzalez, Oscar R.

    1995-01-01

    Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using

  13. Controlled release drug coatings on flexible neural probes.

    PubMed

    Mercanzini, Andre; Reddy, Sai; Velluto, Diana; Colin, Philippe; Maillard, Anne; Bensadoun, Jean-Charles; Bertsch, Arnaud; Hubbell, Jeffrey A; Renaud, Philippe

    2007-01-01

    We present the development, characterization and in vivo validation of a novel drug eluting coating that has been applied to flexible neural probes. The coating consists of drug eluting nanoparticles loaded with an anti-inflammatory drug embedded in a biodegradable polymer. The drug eluting coating is applied to flexible polymer neural probes with platinum electrodes. The drug eluting device is implanted in one hemisphere of a rat, while a control device is implanted in the opposite hemisphere. Impedance measurements are performed to determine the effect of the drug eluting coating on the tissue reaction surrounding the probe and the electrical characteristics of the devices. Probes that are coated with drug eluting coatings show better long term impedance characteristics over control probes. These coatings can be used to increase the reliability and long term success of neural prostheses. PMID:18003541

  14. Determination, diversification and multipotency of mammalian myogenic cells.

    PubMed

    Cossu, G; De Angelis, L; Borello, U; Berarducci, B; Buffa, V; Sonnino, C; Coletta, M; Vivarelli, E; Bouche, M; Lattanzi, L; Tosoni, D; Di Donna, S; Berghella, L; Salvatori, G; Murphy, P; Cusella-De Angelis, M G; Molinaro, M

    2000-01-01

    In amniotes, myogenic commitment appears to be dependent upon signaling from neural tube and dorsal ectoderm, that can be replaced by members of the Wnt family and by Sonic hedgehog. Once committed, myoblasts undergo different fates, in that they can differentiate immediately to form the myotome, or later to give rise to primary and secondary muscle fibers. With fiber maturation, satellite cells are first detected; these cells contribute to fiber growth and regeneration during post-natal life. We will describe recent data, mainly from our laboratory, that suggest a different origin for some of the cells that are incorporated into the muscle fibers during late development. We propose the possibility that these myogenic cells are derived from the vasculature, are multi-potent and become committed to myogenesis by local signaling, when ingressing a differentiating muscle tissue. The implications for fetal and perinatal development of the whole mesoderm will also be discussed. PMID:11061434

  15. Adaptive neural PD control with semiglobal asymptotic stabilization guarantee.

    PubMed

    Pan, Yongping; Yu, Haoyong; Er, Meng Joo

    2014-12-01

    This paper proves that adaptive neural plus proportional-derivative (PD) control can lead to semiglobal asymptotic stabilization rather than uniform ultimate boundedness for a class of uncertain affine nonlinear systems. An integral Lyapunov function-based ideal control law is introduced to avoid the control singularity problem. A variable-gain PD control term without the knowledge of plant bounds is presented to semiglobally stabilize the closed-loop system. Based on a linearly parameterized raised-cosine radial basis function neural network, a key property of optimal approximation is exploited to facilitate stability analysis. It is proved that the closed-loop system achieves semiglobal asymptotic stability by the appropriate choice of control parameters. Compared with previous adaptive approximation-based semiglobal or asymptotic stabilization approaches, our approach not only significantly simplifies control design, but also relaxes constraint conditions on the plant. Two illustrative examples have been provided to verify the theoretical results. PMID:25420247

  16. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  17. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

    1997-01-01

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

  18. Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.

    2000-01-01

    The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.

  19. Applications of neural networks to process control and modeling

    SciTech Connect

    Barnes, C.W.; Brown, S.K.; Flake, G.W.; Jones, R.D.; O'Rourke, M.K.; Lee, Y.C.

    1991-01-01

    Modeling and control of physical processes are universal parts of modern life, from control of chemical plants to riding a bicycle. Often, an effective model of the process is not known so that traditional control theory is of little use. If a process can be represented by a set of a data which captures it behavior over a range of parameter settings, a neural net can inductively model the process and form the basis of an optimization procedure. We present a neural network architecture which is particularly effective in process modeling and control. We discuss its effectiveness in several application areas as well as some of the non-ideal characteristics present in real control problems which effect the form and style of the network architecture and learning algorithm. 8 refs., 6 figs.

  20. An architecture for designing fuzzy logic controllers using neural networks

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

  1. Neural Control Mechanisms and Body Fluid Homeostasis

    NASA Technical Reports Server (NTRS)

    Johnson, Alan Kim

    1998-01-01

    The goal of the proposed research was to study the nature of afferent signals to the brain that reflect the status of body fluid balance and to investigate the central neural mechanisms that process this information for the activation of response systems which restore body fluid homeostasis. That is, in the face of loss of fluids from intracellular or extracellular fluid compartments, animals seek and ingest water and ionic solutions (particularly Na(+) solutions) to restore the intracellular and extracellular spaces. Over recent years, our laboratory has generated a substantial body of information indicating that: (1) a fall in systemic arterial pressure facilitates the ingestion of rehydrating solutions and (2) that the actions of brain amine systems (e.g., norepinephrine; serotonin) are critical for precise correction of fluid losses. Because both acute and chronic dehydration are associated with physiological stresses, such as exercise and sustained exposure to microgravity, the present research will aid in achieving a better understanding of how vital information is handled by the nervous system for maintenance of the body's fluid matrix which is critical for health and well-being.

  2. Backstepping Control Augmented by Neural Networks For Robot Manipulators

    NASA Astrophysics Data System (ADS)

    Belkheiri, Mohammed; Boudjema, Farès

    2008-06-01

    A new control approach is proposed to address the tracking problem of robot manipulators. In this approach, one relies first on a partially known model to the system to be controlled using a backstepping control strategy. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modeling errors. The proposed approach is systematic, and exploits the known nonlinear dynamics to derive the stepwise virtual stabilizing control laws. At the final step, an augmented Lyapunov function is introduced to derive the adaptation laws of the network weights. The effectiveness of the proposed controller is demonstrated through computer simulation on PUMA 560 robot.

  3. Adaptive control of mobile robots using a neural network.

    PubMed

    de Sousa Júnior, C; Hermerly, E M

    2001-06-01

    A Neural Network - based control approach for mobile robot is proposed. The weight adaptation is made on-line, without previous learning. Several possible situations in robot navigation are considered, including uncertainties in the model and presence of disturbance. Weight adaptation laws are presented as well as simulation results. PMID:11574958

  4. Robust Neural Sliding Mode Control of Robot Manipulators

    SciTech Connect

    Nguyen Tran Hiep; Pham Thuong Cat

    2009-03-05

    This paper proposes a robust neural sliding mode control method for robot tracking problem to overcome the noises and large uncertainties in robot dynamics. The Lyapunov direct method has been used to prove the stability of the overall system. Simulation results are given to illustrate the applicability of the proposed method.

  5. Neural dynamic programming applied to rotorcraft flight control and reconfiguration

    NASA Astrophysics Data System (ADS)

    Enns, Russell James

    This dissertation introduces a new rotorcraft flight control methodology based on a relatively new form of neural control, neural dynamic programming (NDP). NDP is an on-line learning control scheme that is in its infancy and has only been applied to simple systems, such as those possessing a single control and a handful of states. This dissertation builds on the existing NDP concept to provide a comprehensive control system framework that can perform well as a learning controller for more realistic and practical systems of higher dimension such as helicopters. To accommodate such complex systems, the dissertation introduces the concept of a trim network that is seamlessly integrated into the NDP control structure and is also trained using this structure. This is the first time that neural networks have been applied to the helicopter control problem as a direct form of control without using other controller methodologies to augment the neural controller and without using order reducing simplifications such as axes decoupling. The dissertation focuses on providing a viable alternative helicopter control system design approach rather than providing extensive comparisons among various available controllers. As such, results showing the system's ability to stabilize the helicopter and to perform command tracking, without explicit comparison to other methods, are presented. In this research, design robustness was addressed by performing simulations under various disturbance conditions. All designs were tested using FLYRT, a sophisticated, industrial-scale, nonlinear, validated model of the Apache helicopter. Though illustrated for helicopters, the NDP control system framework should be applicable to general purpose multi-input multi-output (MIMO) control. In addition, this dissertation tackles the helicopter reconfigurable flight control problem, finding control solutions when the aircraft, and in particular its control actuators, are damaged. Such solutions have

  6. Neural Generalized Predictive Control: A Newton-Raphson Implementation

    NASA Technical Reports Server (NTRS)

    Soloway, Donald; Haley, Pamela J.

    1997-01-01

    An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.

  7. Variable Neural Adaptive Robust Control: A Switched System Approach

    SciTech Connect

    Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

  8. Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

    PubMed

    Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

    2015-08-01

    Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing. PMID:26737158

  9. Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton.

    PubMed

    Yu, Wen; Rosen, Jacob

    2013-04-01

    In order to minimize steady-state error with respect to uncertainties in robot control, proportional-integral-derivative (PID) control needs a big integral gain, or a neural compensator is added to the classical proportional-derivative (PD) control with a large derivative gain. Both of them deteriorate transient performances of the robot control. In this paper, we extend the popular neural PD control into neural PID control. This novel control is a natural combination of industrial linear PID control and neural compensation. The main contributions of this paper are semiglobal asymptotic stability of the neural PID control and local asymptotic stability of the neural PID control with a velocity observer which are proved with standard weight training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an upper limb exoskeleton with this neural PID control is addressed. PMID:23033432

  10. Imaging correlates of neural control of ocular movements.

    PubMed

    Agarwal, Mohit; Ulmer, John L; Chandra, Tushar; Klein, Andrew P; Mark, Leighton P; Mohan, Suyash

    2016-07-01

    The purpose of oculomotor movements is maintenance of clear images on the retina. Beyond this oversimplification, it requires several different types of ocular movements and reflexes to focus objects of interest to the fovea-the only portion of retina capable of sharp and clear vision. The different movements and reflexes that execute this task are the saccades, smooth pursuit movements, fixation, accommodation, and the optokinetic and vestibulo-ocular reflexes. Many different centres in the cerebrum, cerebellum, brainstem and thalami, control these movements via different pathways. At the outset, these mechanisms appear dauntingly complex to a radiologist. However, only a little effort could make it possible to understand these neural controls and empower the reading session. The following review on ocular movements and their neural control will enable radiologists and clinicians to correlate lesions with clinical deficits effectively without being swamped by exhaustive detail. Key Points • Knowledge of cortical and subcortical areas controlling ocular movements is important. • Understanding of neural control of ocular movements makes a good foundation. • Awareness of anatomic areas controlling ocular movements helps in clinico-radiologic correlation. PMID:26396109

  11. Computation and control with neural nets

    SciTech Connect

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-10-04

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.

  12. DCS-Neural-Network Program for Aircraft Control and Testing

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    2006-01-01

    A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

  13. Attentional control of the processing of neural and emotional stimuli.

    PubMed

    Pessoa, Luiz; Kastner, Sabine; Ungerleider, Leslie G

    2002-12-01

    A typical scene contains many different objects that compete for neural representation due to the limited processing capacity of the visual system. At the neural level, competition among multiple stimuli is evidenced by the mutual suppression of their visually evoked responses and occurs most strongly at the level of the receptive field. The competition among multiple objects can be biased by both bottom-up sensory-driven mechanisms and top-down influences, such as selective attention. Functional brain imaging studies reveal that biasing signals due to selective attention can modulate neural activity in visual cortex not only in the presence but also in the absence of visual stimulation. Although the competition among stimuli for representation is ultimately resolved within visual cortex, the source of top-down biasing signals likely derives from a distributed network of areas in frontal and parietal cortex. Competition suggests that once attentional resources are depleted, no further processing is possible. Yet, existing data suggest that emotional stimuli activate brain regions "automatically," largely immune from attentional control. We tested the alternative possibility, namely, that the neural processing of stimuli with emotional content is not automatic and instead requires some degree of attention. Our results revealed that, contrary to the prevailing view, all brain regions responding differentially to emotional faces, including the amygdala, did so only when sufficient attentional resources were available to process the faces. Thus, similar to the processing of other stimulus categories, the processing of facial expression is under top-down control. PMID:12433381

  14. Controlling neural activity in Caenorhabditis elegans to evoke chemotactic behavior

    NASA Astrophysics Data System (ADS)

    Kocabas, Askin; Shen, Ching-Han; Guo, Zengcai V.; Ramanathan, Sharad

    2013-03-01

    Animals locate and track chemoattractive gradients in the environment to find food. With its simple nervous system, Caenorhabditis elegans is a good model system in which to understand how the dynamics of neural activity control this search behavior. To understand how the activity in its interneurons coordinate different motor programs to lead the animal to food, here we used optogenetics and new optical tools to manipulate neural activity directly in freely moving animals to evoke chemotactic behavior. By deducing the classes of activity patterns triggered during chemotaxis and exciting individual neurons with these patterns, we identified interneurons that control the essential locomotory programs for this behavior. Notably, we discovered that controlling the dynamics of activity in just one interneuron pair was sufficient to force the animal to locate, turn towards and track virtual light gradients.

  15. Optogenetic feedback control of neural activity

    PubMed Central

    Newman, Jonathan P; Fong, Ming-fai; Millard, Daniel C; Whitmire, Clarissa J; Stanley, Garrett B; Potter, Steve M

    2015-01-01

    Optogenetic techniques enable precise excitation and inhibition of firing in specified neuronal populations and artifact-free recording of firing activity. Several studies have suggested that optical stimulation provides the precision and dynamic range requisite for closed-loop neuronal control, but no approach yet permits feedback control of neuronal firing. Here we present the ‘optoclamp’, a feedback control technology that provides continuous, real-time adjustments of bidirectional optical stimulation in order to lock spiking activity at specified targets over timescales ranging from seconds to days. We demonstrate how this system can be used to decouple neuronal firing levels from ongoing changes in network excitability due to multi-hour periods of glutamatergic or GABAergic neurotransmission blockade in vitro as well as impinging vibrissal sensory drive in vivo. This technology enables continuous, precise optical control of firing in neuronal populations in order to disentangle causally related variables of circuit activation in a physiologically and ethologically relevant manner. DOI: http://dx.doi.org/10.7554/eLife.07192.001 PMID:26140329

  16. Utilizing Autologous Multipotent Mesenchymal Stromal Cells and β-Tricalcium Phosphate Scaffold in Human Bone Defects: A Prospective, Controlled Feasibility Trial

    PubMed Central

    Šponer, Pavel; Kučera, Tomáš; Brtková, Jindra; Urban, Karel; Palička, Vladimír; Kočí, Zuzana; Syka, Michael; Syková, Eva

    2016-01-01

    The purpose of this prospective controlled study was to compare healing quality following the implantation of ultraporous β-tricalcium phosphate, containing either expanded autologous mesenchymal stromal cells (trial group, 9 patients) or β-tricalcium phosphate alone (control group, 9 patients), into femoral defects during revision total hip arthroplasty. Both groups were assessed using the Harris Hip Score, radiography, and DEXA scanning at 6 weeks and 3, 6, and 12 months postoperatively. A significant difference in the bone defect healing was observed between both groups of patients (P < 0.05). In the trial group, trabecular remodeling was found in all nine patients and in the control group, in 1 patient only. Whereas, over the 12-month follow-up period, no significant difference was observed between both groups of patients in terms of the resorption of β-tricalcium phosphate, the significant differences were documented in the presence of radiolucency and bone trabeculation through the defect (P < 0.05). Using autologous mesenchymal stromal cells combined with a β-tricalcium phosphate scaffold is a feasible, safe, and effective approach for management of bone defects with compromised microenvironment. The clinical trial was registered at the EU Clinical Trials Register before patient recruitment has begun (EudraCT number 2012-005599-33). PMID:27144159

  17. Molecular Control of the Neural Crest and Peripheral Nervous System Development

    PubMed Central

    Newbern, Jason M.

    2015-01-01

    A transient and unique population of multipotent stem cells, known as neural crest cells (NCCs), generate a bewildering array of cell types during vertebrate development. An attractive model among developmental biologists, the study of NCC biology has provided a wealth of knowledge regarding the cellular and molecular mechanisms important for embryogenesis. Studies in numerous species have defined how distinct phases of NCC specification, proliferation, migration, and survival contribute to the formation of multiple functionally distinct organ systems. NCC contributions to the peripheral nervous system (PNS) are well known. Critical developmental processes have been defined that provide outstanding models for understanding how extracellular stimuli, cell–cell interactions, and transcriptional networks cooperate to direct cellular diversification and PNS morphogenesis. Dissecting the complex extracellular and intracellular mechanisms that mediate the formation of the PNS from NCCs may have important therapeutic implications for neurocristopathies, neuropathies, and certain forms of cancer. PMID:25662262

  18. Neural networks for feedback feedforward nonlinear control systems.

    PubMed

    Parisini, T; Zoppoli, R

    1994-01-01

    This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method. PMID:18267810

  19. Sinusoidal modulation control method in a chaotic neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Qihanyue; Xie, Xiaoping; Zhu, Ping; Chen, Hongping; He, Guoguang

    2014-08-01

    Chaotic neural networks (CNNs) have chaotic dynamic associative memory properties: The memory states appear non-periodically, and cannot be converged to a stored pattern. Thus, it is necessary to control chaos in a CNN in order to recognize associative memory. In this paper, a novel control method, the sinusoidal modulation control method, has been proposed to control chaos in a CNN. In this method, a sinusoidal wave simplified from brain waves is used as a control signal to modulate a parameter of the CNN. The simulation results demonstrate the effectiveness of this control method. The controlled CNN can be applied to information processing. Moreover, the method provides a way to associate brain waves by controlling CNNs.

  20. Neural Evidence for the Flexible Control of Mental Representations.

    PubMed

    Lewis-Peacock, Jarrod A; Drysdale, Andrew T; Postle, Bradley R

    2015-10-01

    This study was designed to explore neural evidence for the simultaneous engagement of multiple mental codes while retaining a visual object in short-term memory (STM) and, if successful, to explore the neural bases of strategic prioritization among these codes. We used multivariate pattern analysis of fMRI data to track patterns of brain activity associated with three common mental codes: visual, verbal, and semantic. When participants did not know which dimension of a sample stimulus would be tested, patterns of brain activity during the memory delay indicated that a visual representation was quickly augmented with both verbal and semantic re-representations of the stimulus. The verbal code emerged as most highly activated, consistent with a canonical visual-to-phonological recoding operation in STM. If participants knew which dimension of a sample stimulus would be tested, brain activity patterns were biased toward the probe-relevant stimulus dimension. Interestingly, probe-irrelevant neural states persisted at an intermediate level of activation when they were potentially relevant later in the trial, but dropped to baseline when cued to be irrelevant. These results reveal the neural dynamics underlying the creation and retention of mental codes, and they illustrate the flexible control that humans can exert over these representations. PMID:24935778

  1. Prediction and control of neural responses to pulsatile electrical stimulation

    NASA Astrophysics Data System (ADS)

    Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  2. Neural mechanisms underlying auditory feedback control of speech

    PubMed Central

    Reilly, Kevin J.; Guenther, Frank H.

    2013-01-01

    The neural substrates underlying auditory feedback control of speech were investigated using a combination of functional magnetic resonance imaging (fMRI) and computational modeling. Neural responses were measured while subjects spoke monosyllabic words under two conditions: (i) normal auditory feedback of their speech, and (ii) auditory feedback in which the first formant frequency of their speech was unexpectedly shifted in real time. Acoustic measurements showed compensation to the shift within approximately 135 ms of onset. Neuroimaging revealed increased activity in bilateral superior temporal cortex during shifted feedback, indicative of neurons coding mismatches between expected and actual auditory signals, as well as right prefrontal and Rolandic cortical activity. Structural equation modeling revealed increased influence of bilateral auditory cortical areas on right frontal areas during shifted speech, indicating that projections from auditory error cells in posterior superior temporal cortex to motor correction cells in right frontal cortex mediate auditory feedback control of speech. PMID:18035557

  3. Neural rotational speed control for wave energy converters

    NASA Astrophysics Data System (ADS)

    Amundarain, M.; Alberdi, M.; Garrido, A. J.; Garrido, I.

    2011-02-01

    Among the benefits arising from an increasing use of renewable energy are: enhanced security of energy supply, stimulation of economic growth, job creation and protection of the environment. In this context, this study analyses the performance of an oscillating water column device for wave energy conversion in function of the stalling behaviour in Wells turbines, one of the most widely used turbines in wave energy plants. For this purpose, a model of neural rotational speed control system is presented, simulated and implemented. This scheme is employed to appropriately adapt the speed of the doubly-fed induction generator coupled to the turbine according to the pressure drop entry, so as to avoid the undesired stalling behaviour. It is demonstrated that the proposed neural rotational speed control design adequately matches the desired relationship between the slip of the doubly-fed induction generator and the pressure drop input, improving the power generated by the turbine generator module.

  4. Neural Controller For Adaptive Sensory-Motor Coordination

    NASA Astrophysics Data System (ADS)

    Kuperstein, Michael; Rubinstein, Jorge

    1989-03-01

    We present a theory and prototype of a neural controller called INFANT that learns sensory-motor coordination from its own experience. INFANT adapts to unforeseen changes in the geometry of the physical motor system and to the location, orientation, shape and size of objects. It can learn to accurately grasp an elongated object without any information about the geometry of the physical sensory-motor system. This new neural controller relies on the self-consistency between sensory and motor signals to achieve unsupervised learning. It is designed to be generalized for coordinating any number of sensory inputs with limbs of any number of joints. INFANT is implemented with an image processor, stereo cameras and a five degree-of freedom robot arm. Its average grasping accuracy after learning is 3% of the arm's length in position and 6 degrees in orientation.

  5. Neural optimal control of flexible spacecraft slew maneuver

    NASA Astrophysics Data System (ADS)

    Nayeri, M. Reza Dehghan; Alasty, Aria; Daneshjou, Kamran

    2004-11-01

    This paper deals with the problem of optimal large-angle single-axis maneuvers of a flexible spacecraft with simultaneous vibration suppression of elastic modes. A spacecraft model with a cylindrical hub and one flexible appendage and tip mass is considered. Gravity gradient torque is considered as a disturbance torque. Multilayer perceptron neural networks are used to design a Neural Optimal Controller (NOC) for this multivariable non-linear maneuver. For NOC training, an off-line training procedure based on backpropagation through time algorithm is developed to minimize the general quadratic cost function in forward and backward pass stages. The proposed controller is also applicable to simultaneous multi-axis reorientation of a flexible spacecraft. Simulation results are presented to show that very fast reference pitch angle trajectory tracking and vibration suppression are accomplished.

  6. Molecular and neural control of sexually dimorphic social behaviors.

    PubMed

    Yang, Taehong; Shah, Nirao M

    2016-06-01

    Sexually reproducing animals exhibit sex differences in behavior. Sexual dimorphisms in mating, aggression, and parental care directly contribute to reproductive success of the individual and survival of progeny. In this review, we discuss recent advances in our understanding of the molecular and neural network mechanisms underlying these behaviors in mice. Notable advances include novel insights into the sensory control of social interactions and the identification of molecularly-specified neuronal populations in the brain that control mating, aggression, and parental behaviors. In the case of the latter, these advances mark a watershed because scientists can now focus on discrete neural pathways in an effort to understand how the brain encodes these fundamental social behaviors. PMID:27162162

  7. Topographical strategies to control neural outgrowth.

    PubMed

    Roccasalvo, I Morana; Sergi, P N; Tonazzini, I; Cecchini, M; Micera, S

    2015-08-01

    In this work a synergistic approach is used to investigate how directional anisotropic surfaces (i.e., nanogratings) control the alignment of PC12 neurites. Finite Element models were used to assess the distribution of stresses in non-spread growth cones and filopodia. The stress field was assumed to be the main triggering cause fostering the increase and stabilization of filopodia, so the local stress maxima were directly related to the neuritic orientation. Moreover, a computational framework was implemented within an open source Java environment (CX3D), and in silico simulations were carried out to reproduce and predict biological experiments. No significant differences were found between biological experiments and in silico simulations (alignment angle, p = 0.4685; tortuosity, p = 0.9075) with a standard level of confidence (95%). PMID:26737940

  8. A biologically inspired neural network controller for ballistic arm movements

    PubMed Central

    Bernabucci, Ivan; Conforto, Silvia; Capozza, Marco; Accornero, Neri; Schmid, Maurizio; D'Alessio, Tommaso

    2007-01-01

    Background In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented. Methods The developed system is composed of three main computational blocks: 1) a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2) a pulse generator, which is responsible for the creation of muscular synergies; and 3) a limb model based on two joints (two degrees of freedom) and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. Results The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. Conclusion The proposed system has been shown to properly simulate the development of internal models and to control

  9. Control strategies for underactuated neural ensembles driven by optogenetic stimulation

    PubMed Central

    Ching, ShiNung; Ritt, Jason T.

    2013-01-01

    Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded) neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications. PMID:23576956

  10. Neural Network Based Montioring and Control of Fluidized Bed.

    SciTech Connect

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01

    The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to

  11. Neural substrates linking balance control and anxiety

    NASA Technical Reports Server (NTRS)

    Balaban, Carey D.

    2002-01-01

    This communication provides an update of our understanding of the neurological bases for the close association between balance control and anxiety. New data suggest that a vestibulo-recipient region of the parabrachial nucleus (PBN) contains cells that respond to body rotation and position relative to gravity. The PBN, with its reciprocal relationships with the extended central amygdaloid nucleus, infralimbic cortex, and hypothalamus, appears to be an important node in a primary network that processes convergent vestibular, somatic, and visceral information processing to mediate avoidance conditioning, anxiety, and conditioned fear responses. Noradrenergic and serotonergic projections to the vestibular nuclei also have parallel connections with anxiety pathways. The coeruleo-vestibular pathway originates in caudal locus coeruleus (LC) and provides regionally specialized noradrenergic input to the vestibular nuclei, which likely mediate effects of alerting and vigilance on the sensitivity of vestibulo-motor circuits. Both serotonergic and nonserotonergic pathways from the dorsal raphe nucleus and the nucleus raphe obscurus also project differentially to the vestibular nuclei, and 5-HT(2A) receptors are expressed in amygdaloid and cortical targets of the PBN. It is proposed that the dorsal raphe nucleus pathway contributes to both (a) a tradeoff between motor and sensory (information gathering) aspects of responses to self-motion and (b) a calibration of the sensitivity of affective responses to aversive aspects of motion. This updated neurologic model continues to be a synthetic schema for investigating the neurological and neurochemical bases for comorbidity of balance disorders and anxiety disorders.

  12. Neural control of chronic stress adaptation

    PubMed Central

    Herman, James P.

    2013-01-01

    Stress initiates adaptive processes that allow the organism to physiologically cope with prolonged or intermittent exposure to real or perceived threats. A major component of this response is repeated activation of glucocorticoid secretion by the hypothalamo-pituitary-adrenocortical (HPA) axis, which promotes redistribution of energy in a wide range of organ systems, including the brain. Prolonged or cumulative increases in glucocorticoid secretion can reduce benefits afforded by enhanced stress reactivity and eventually become maladaptive. The long-term impact of stress is kept in check by the process of habituation, which reduces HPA axis responses upon repeated exposure to homotypic stressors and likely limits deleterious actions of prolonged glucocorticoid secretion. Habituation is regulated by limbic stress-regulatory sites, and is at least in part glucocorticoid feedback-dependent. Chronic stress also sensitizes reactivity to new stimuli. While sensitization may be important in maintaining response flexibility in response to new threats, it may also add to the cumulative impact of glucocorticoids on the brain and body. Finally, unpredictable or severe stress exposure may cause long-term and lasting dysregulation of the HPA axis, likely due to altered limbic control of stress effector pathways. Stress-related disorders, such as depression and PTSD, are accompanied by glucocorticoid imbalances and structural/ functional alterations in limbic circuits that resemble those seen following chronic stress, suggesting that inappropriate processing of stressful information may be part of the pathological process. PMID:23964212

  13. Neural Control Adaptation to Motor Noise Manipulation

    PubMed Central

    Hasson, Christopher J.; Gelina, Olga; Woo, Garrett

    2016-01-01

    Antagonistic muscular co-activation can compensate for movement variability induced by motor noise at the expense of increased energetic costs. Greater antagonistic co-activation is commonly observed in older adults, which could be an adaptation to increased motor noise. The present study tested this hypothesis by manipulating motor noise in 12 young subjects while they practiced a goal-directed task using a myoelectric virtual arm, which was controlled by their biceps and triceps muscle activity. Motor noise was increased by increasing the coefficient of variation (CV) of the myoelectric signals. As hypothesized, subjects adapted by increasing antagonistic co-activation, and this was associated with reduced noise-induced performance decrements. A second hypothesis was that a virtual decrease in motor noise, achieved by smoothing the myoelectric signals, would have the opposite effect: co-activation would decrease and motor performance would improve. However, the results showed that a decrease in noise made performance worse instead of better, with no change in co-activation. Overall, these findings suggest that the nervous system adapts to virtual increases in motor noise by increasing antagonistic co-activation, and this preserves motor performance. Reducing noise may have failed to benefit performance due to characteristics of the filtering process itself, e.g., delays are introduced and muscle activity bursts are attenuated. The observed adaptations to increased noise may explain in part why older adults and many patient populations have greater antagonistic co-activation, which could represent an adaptation to increased motor noise, along with a desire for increased joint stability. PMID:26973487

  14. Neural Control Adaptation to Motor Noise Manipulation.

    PubMed

    Hasson, Christopher J; Gelina, Olga; Woo, Garrett

    2016-01-01

    Antagonistic muscular co-activation can compensate for movement variability induced by motor noise at the expense of increased energetic costs. Greater antagonistic co-activation is commonly observed in older adults, which could be an adaptation to increased motor noise. The present study tested this hypothesis by manipulating motor noise in 12 young subjects while they practiced a goal-directed task using a myoelectric virtual arm, which was controlled by their biceps and triceps muscle activity. Motor noise was increased by increasing the coefficient of variation (CV) of the myoelectric signals. As hypothesized, subjects adapted by increasing antagonistic co-activation, and this was associated with reduced noise-induced performance decrements. A second hypothesis was that a virtual decrease in motor noise, achieved by smoothing the myoelectric signals, would have the opposite effect: co-activation would decrease and motor performance would improve. However, the results showed that a decrease in noise made performance worse instead of better, with no change in co-activation. Overall, these findings suggest that the nervous system adapts to virtual increases in motor noise by increasing antagonistic co-activation, and this preserves motor performance. Reducing noise may have failed to benefit performance due to characteristics of the filtering process itself, e.g., delays are introduced and muscle activity bursts are attenuated. The observed adaptations to increased noise may explain in part why older adults and many patient populations have greater antagonistic co-activation, which could represent an adaptation to increased motor noise, along with a desire for increased joint stability. PMID:26973487

  15. Asymmetric Distribution of GFAP in Glioma Multipotent Cells

    PubMed Central

    Guichet, Pierre-Olivier; Guelfi, Sophie; Ripoll, Chantal; Teigell, Marisa; Sabourin, Jean-Charles; Bauchet, Luc; Rigau, Valérie; Rothhut, Bernard; Hugnot, Jean-Philippe

    2016-01-01

    Asymmetric division (AD) is a fundamental mechanism whereby unequal inheritance of various cellular compounds during mitosis generates unequal fate in the two daughter cells. Unequal repartitions of transcription factors, receptors as well as mRNA have been abundantly described in AD. In contrast, the involvement of intermediate filaments in this process is still largely unknown. AD occurs in stem cells during development but was also recently observed in cancer stem cells. Here, we demonstrate the asymmetric distribution of the main astrocytic intermediate filament, namely the glial fibrillary acid protein (GFAP), in mitotic glioma multipotent cells isolated from glioblastoma (GBM), the most frequent type of brain tumor. Unequal mitotic repartition of GFAP was also observed in mice non-tumoral neural stem cells indicating that this process occurs across species and is not restricted to cancerous cells. Immunofluorescence and videomicroscopy were used to capture these rare and transient events. Considering the role of intermediate filaments in cytoplasm organization and cell signaling, we propose that asymmetric distribution of GFAP could possibly participate in the regulation of normal and cancerous neural stem cell fate. PMID:26953813

  16. An application of neural networks to process and materials control

    SciTech Connect

    Howell, J.A.; Whiteson, R.

    1991-01-01

    Process control consists of two basic elements: a model of the process and knowledge of the desired control algorithm. In some cases the level of the control algorithm is merely supervisory, as in an alarm-reporting or anomaly-detection system. If the model of the process is known, then a set of equations may often be solved explicitly to provide the control algorithm. Otherwise, the model has to be discovered through empirical studies. Neural networks have properties that make them useful in this application. They can learn (make internal models from experience or observations). The problem of anomaly detection in materials control systems fits well into this general control framework. To successfully model a process with a neutral network, a good set of observables must be chosen. These observables must in some sense adequately span the space of representable events, so that a signature metric can be built for normal operation. In this way, a non-normal event, one that does not fit within the signature, can be detected. In this paper, we discuss the issues involved in applying a neural network model to anomaly detection in materials control systems. These issues include data selection and representation, network architecture, prediction of events, the use of simulated data, and software tools. 10 refs., 4 figs., 1 tab.

  17. An integrated architecture of adaptive neural network control for dynamic systems

    SciTech Connect

    Ke, Liu; Tokar, R.; Mcvey, B.

    1994-07-01

    In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.

  18. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  19. Combustion control of municipal incinerators by fuzzy neural network logic

    SciTech Connect

    Chang, N.B.; Chang, Y.H.

    1996-12-31

    The successful operation of mass burn waterwall incinerators involves many uncertain factors. Not only the physical composition and chemical properties of the refuse but also the complexity of combustion mechanism would significantly influence the performance of waste treatment. Due to the rising concerns of dioxin/furan emissions from municipal incinerators, improved combustion control algorithms, such as fuzzy and its fusion control technologies, have gradually received attention in the scientific community. This paper describes a fuzzy and neural network control logic for the refuse combustion process in a mass burn waterwall incinerator. It is anticipated that this system can also be easily applied to several other types of municipal incinerators, such as modular, rotary kiln, RDF and fluidized bed incinerators, by slightly modified steps. Partial performance of this designed controller is tested by computer simulation using identified process model in this analysis. Process control could be sensitive especially for the control of toxic substance emissions, such as dioxin and furans.

  20. Interlimb coordination, gait, and neural control of quadrupedalism in chimpanzees.

    PubMed

    Shapiro, L J; Anapol, F C; Jungers, W L

    1997-02-01

    Interlimb coordination is directly relevant to the understanding of the neural control of locomotion, but few studies addressing this topic for nonhuman primates are available, and no data exist for any hominoid other than humans. As a follow-up to Jungers and Anapol's ([1985] Am. J. Phys. Anthropol. 67:89-97) analysis on a lemur and talapoin monkey, we describe here the patterns of interlimb coordination in two chimpanzees as revealed by electromyography. Like the lemur and talapoin monkey, ipsilateral limb coupling in chimpanzees is characterized by variability about preferred modes within individual gaits. During symmetrical gaits, limb coupling patterns in the chimpanzee are also influenced by kinematic differences in hindlimb placement ("overstriding"). These observations reflect the neurological constraints placed on locomotion but also emphasize the overall flexibility of locomotor neural mechanisms. Interlimb coordination patterns are also species-specific, exhibiting significant differences among primate taxa and between primates and cats. Interspecific differences may be suggestive of phylogenetic divergence in the basic mechanisms for neural control of locomotion, but do not preclude morphological explanations for observed differences in interlimb coordination across species. PMID:9066899

  1. Flexible neural mechanisms of cognitive control within human prefrontal cortex.

    PubMed

    Braver, Todd S; Paxton, Jessica L; Locke, Hannah S; Barch, Deanna M

    2009-05-01

    A major challenge in research on executive control is to reveal its functional decomposition into underlying neural mechanisms. A typical assumption is that this decomposition occurs solely through anatomically based dissociations. Here we tested an alternative hypothesis that different cognitive control processes may be implemented within the same brain regions, with fractionation and dissociation occurring on the basis of temporal dynamics. Regions within lateral prefrontal cortex (PFC) were examined that, in a prior study, exhibited contrasting temporal dynamics between older and younger adults during performance of the AX-CPT cognitive control task. The temporal dynamics in younger adults fit a proactive control pattern (primarily cue-based activation), whereas in older adults a reactive control pattern was found (primarily probe-based activation). In the current study, we found that following a period of task-strategy training, these older adults exhibited a proactive shift within a subset of the PFC regions, normalizing their activity dynamics toward young adult patterns. Conversely, under conditions of penalty-based monetary incentives, the younger adults exhibited a reactive shift some of the same regions, altering their temporal dynamics toward the older adult baseline pattern. These experimentally induced crossover patterns of temporal dynamics provide strong support for dual modes of cognitive control that can be flexibly shifted within PFC regions, via modulation of neural responses to changing task conditions or behavioral goals. PMID:19380750

  2. Neural correlates of cognitive style and flexible cognitive control.

    PubMed

    Shin, Gyeonghee; Kim, Chobok

    2015-06-01

    Human abilities of flexible cognitive control are associated with appropriately regulating the amount of cognitive control required in response to contextual demands. In the context of conflicting situations, for instance, the amount of cognitive control increases according to the level of previously experienced conflict, resulting in optimized performance. We explored whether the amount of cognitive control in conflict resolution was related to individual differences in cognitive style that were determined with the Object-Spatial-Verbal cognitive style questionnaire. In this functional magnetic resonance imaging (fMRI) study, a version of the color-word Stroop task, which evokes conflict between color and verbal components, was employed to explore whether individual preferences for distracting information were related to the increases in neural conflict adaptation in cognitive control network regions. The behavioral data revealed that the more the verbal style was preferred, the greater the conflict adaptation effect was observed, especially when the current trial type was congruent. Consistent with the behavioral data, the imaging results demonstrated increased neural conflict adaptation effects in task-relevant network regions, including the left dorsolateral prefrontal cortex, left fusiform gyrus, and left precuneus, as the preference for verbal style increased. These results provide new evidence that flexible cognitive control is closely associated with individuals' preference of cognitive style. PMID:25812714

  3. Light, heat, action: neural control of fruit fly behaviour

    PubMed Central

    Owald, David; Lin, Suewei; Waddell, Scott

    2015-01-01

    The fruit fly Drosophila melanogaster has emerged as a popular model to investigate fundamental principles of neural circuit operation. The sophisticated genetics and small brain permit a cellular resolution understanding of innate and learned behavioural processes. Relatively recent genetic and technical advances provide the means to specifically and reproducibly manipulate the function of many fly neurons with temporal resolution. The same cellular precision can also be exploited to express genetically encoded reporters of neural activity and cell-signalling pathways. Combining these approaches in living behaving animals has great potential to generate a holistic view of behavioural control that transcends the usual molecular, cellular and systems boundaries. In this review, we discuss these approaches with particular emphasis on the pioneering studies and those involving learning and memory. PMID:26240426

  4. A neural-network approach to robotic control

    NASA Technical Reports Server (NTRS)

    Graham, D. P. W.; Deleuterio, G. M. T.

    1993-01-01

    An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

  5. Neural net controller for inlet pressure control of rocket engine testing

    NASA Technical Reports Server (NTRS)

    Trevino, Luis C.

    1994-01-01

    Many dynamic systems operate in select operating regions, each exhibiting characteristic modes of behavior. It is traditional to employ standard adjustable gain proportional-integral-derivative (PID) loops in such systems where no apriori model information is available. However, for controlling inlet pressure for rocket engine testing, problems in fine tuning, disturbance accommodation, and control gains for new profile operating regions (for research and development) are typically encountered. Because of the capability of capturing I/O peculiarities, using NETS, a back propagation trained neural network is specified. For select operating regions, the neural network controller is simulated to be as robust as the PID controller. For a comparative analysis, the higher order moment neural array (HOMNA) method is used to specify a second neural controller by extracting critical exemplars from the I/O data set. Furthermore, using the critical exemplars from the HOMNA method, a third neural controller is developed using NETS back propagation algorithm. All controllers are benchmarked against each other.

  6. Discrete neural dynamic programming in wheeled mobile robot control

    NASA Astrophysics Data System (ADS)

    Hendzel, Zenon; Szuster, Marcin

    2011-05-01

    In this paper we propose a discrete algorithm for a tracking control of a two-wheeled mobile robot (WMR), using an advanced Adaptive Critic Design (ACD). We used Dual-Heuristic Programming (DHP) algorithm, that consists of two parametric structures implemented as Neural Networks (NNs): an actor and a critic, both realized in a form of Random Vector Functional Link (RVFL) NNs. In the proposed algorithm the control system consists of the DHP adaptive critic, a PD controller and a supervisory term, derived from the Lyapunov stability theorem. The supervisory term guaranties a stable realization of a tracking movement in a learning phase of the adaptive critic structure and robustness in face of disturbances. The discrete tracking control algorithm works online, uses the WMR model for a state prediction and does not require a preliminary learning. Verification has been conducted to illustrate the performance of the proposed control algorithm, by a series of experiments on the WMR Pioneer 2-DX.

  7. Intelligent control based on fuzzy logic and neural net theory

    NASA Technical Reports Server (NTRS)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  8. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  9. Control aspects of motor neural prosthesis: sensory interface.

    PubMed

    Popović, Dejan B; Dosen, Strahinja; Popović, Mirjana B; Stefanović, Filip; Kojović, Jovana

    2007-01-01

    A neural prosthesis (NP) has two applications: permanent assistance of function, and temporary assistance that contributes to long-term recovery of function. Here, we address control issues for a therapeutic NP which uses surface electrodes. We suggest that the effective NP for therapy needs to implement rule-based control. Rule-based control relies on the triggering of preprogrammed sequences of electrical stimulation by the sensory signals. The sensory system in the therapeutic NP needs to be simple for installation, allow self-calibration, it must be robust, and sufficiently redundant in order to guarantee safe operation. The sensory signals need to generate control signals; hence, sensory fusion is needed. MEMS technology today provides sensors that fulfill the technical requirements (accelerometers, gyroscopes, force sensing resistors). Therefore, the task was to design a sensory signal processing method from the mentioned solid state sensors that would recognize phases during the gait cycle. This is necessary for the control of multi channel electrical stimulation. The sensory fusion consists of the following two phases: 1) estimation of vertical and horizontal components of the ground reaction force, center of pressure, and joint angles from the solid-state sensors, and 2) fusion of the estimated signals into a sequence of command signals. The first phase was realized by the use of artificial neural networks and adaptive neuro-fuzzy inference systems, while the second by the use of inductive learning described in our earlier work [1]. PMID:18002969

  10. Statistical process control using optimized neural networks: a case study.

    PubMed

    Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

    2014-09-01

    The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. PMID:24210290

  11. Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2007-01-01

    The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.

  12. Modeling, monitoring and control based on neural networks

    SciTech Connect

    Fu, Chi Yung

    1995-04-14

    The cost of a fabrication line such as one in a semiconductor house has increased dramatically over the years and it is possibly already past the point that some new start-up company can have sufficient capital to build a new fabrication line. Such capital-intensive manufacturing needs better utilization of resources and management of equipment to maximize its productivity. In order to maximize the return from such a capital-intensive manufacturing line, we need to address the following: (1) increasing the yield, (2) enhancing the flexibility of the fabrication line, (3) improving quality, and finally (4) minimizing the down time of the processing equipment. Because of the significant advances now made in the fields of artificial neural networks, fuzzy logic, machine learning and genetic algorithms, we advocate the use of these new tools to in manufacturing. We term the applications of these and other tools that mimic human intelligence to manufacturing neural manufacturing. This paper will address the effort at Lawrence Livermore National Laboratory (LLNL) to use artificial neural networks to address certain semiconductor process modeling, monitoring and control questions.

  13. Internal models for interpreting neural population activity during sensorimotor control

    PubMed Central

    Golub, Matthew D; Yu, Byron M; Chase, Steven M

    2015-01-01

    To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects’ internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output. DOI: http://dx.doi.org/10.7554/eLife.10015.001 PMID:26646183

  14. Drive reinforcement neural networks for reactor control. Final report

    SciTech Connect

    Williams, J.G.; Jouse, W.C.

    1995-02-01

    In view of the loss of the third year funding, the scope of the project goals has been revised. The revision in project scope no longer allows for the detailed modeling of the EBR-11 start-up task that was originally envisaged. The authors are continuing, however, to model the control of the rapid power ascent of the University of Arizona TRIGA reactor using a model-based controller and using a drive reinforcement neural network. These will be combined during the concluding period of the project into a hierarchical control architecture. In addition, the modeling of a PWR feedwater heater has continued, and an autonomous fault-tolerant software architecture for its control has been proposed.

  15. Analog neural network control method proposed for use in a backup satellite control mode

    SciTech Connect

    Frigo, J.R.; Tilden, M.W.

    1998-03-01

    The authors propose to use an analog neural network controller implemented in hardware, independent of the active control system, for use in a satellite backup control mode. The controller uses coarse sun sensor inputs. The field of view of the sensors activate the neural controller, creating an analog dead band with respect to the direction of the sun on each axis. This network controls the orientation of the vehicle toward the sunlight to ensure adequate power for the system. The attitude of the spacecraft is stabilized with respect to the ambient magnetic field on orbit. This paper develops a model of the controller using real-time coarse sun sensor data and a dynamic model of a prototype system based on a satellite system. The simulation results and the feasibility of this control method for use in a satellite backup control mode are discussed.

  16. Upper Torso Control for HOAP-2 Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Sandoval, Steven P.

    2005-01-01

    Humanoid robots have similar physical builds and motion patterns as humans. Not only does this provide a suitable operating environment for the humanoid but it also opens up many research doors on how humans function. The overall objective is replacing humans operating in unsafe environments. A first target application is assembly of structures for future lunar-planetary bases. The initial development platform is a Fujitsu HOAP-2 humanoid robot. The goal for the project is to demonstrate the capability of a HOAP-2 to autonomously construct a cubic frame using provided tubes and joints. This task will require the robot to identify several items, pick them up, transport them to the build location, then properly assemble the structure. The ability to grasp and assemble the pieces will require improved motor control and the addition of tactile feedback sensors. In recent years, learning-based control is becoming more and more popular; for implementing this method we will be using the Adaptive Neural Fuzzy Inference System (ANFIS). When using neural networks for control, no complex models of the system must be constructed in advance-only input/output relationships are required to model the system.

  17. On the neural control of social emotional behavior.

    PubMed

    Roelofs, Karin; Minelli, Alessandra; Mars, Rogier B; van Peer, Jacobien; Toni, Ivan

    2009-03-01

    It is known that the orbitofrontal cortex (OFC) is crucially involved in emotion regulation. However, the specific role of the OFC in controlling the behavior evoked by these emotions, such as approach-avoidance (AA) responses, remains largely unexplored. We measured behavioral and neural responses (using fMRI) during the performance of a social task, a reaction time (RT) task where subjects approached or avoided visually presented emotional faces by pulling or pushing a joystick, respectively. RTs were longer for affect-incongruent responses (approach angry faces and avoid happy faces) as compared to affect-congruent responses (approach-happy; avoid-angry). Moreover, affect-incongruent responses recruited increased activity in the left lateral OFC. These behavioral and neural effects emerged only when the subjects responded explicitly to the emotional value of the faces (AA-task) and largely disappeared when subjects responded to an affectively irrelevant feature of the faces during a control (gender evaluation: GE) task. Most crucially, the size of the OFC-effect correlated positively with the size of the behavioral costs of approaching angry faces. These findings qualify the role of the lateral OFC in the voluntary control of social-motivational behavior, emphasizing the relevance of this region for selecting rule-driven stimulus-response associations, while overriding automatic (affect-congruent) stimulus-response mappings. PMID:19047074

  18. Entropy, Ergodicity, and Stem Cell Multipotency

    NASA Astrophysics Data System (ADS)

    Ridden, Sonya J.; Chang, Hannah H.; Zygalakis, Konstantinos C.; MacArthur, Ben D.

    2015-11-01

    Populations of mammalian stem cells commonly exhibit considerable cell-cell variability. However, the functional role of this diversity is unclear. Here, we analyze expression fluctuations of the stem cell surface marker Sca1 in mouse hematopoietic progenitor cells using a simple stochastic model and find that the observed dynamics naturally lie close to a critical state, thereby producing a diverse population that is able to respond rapidly to environmental changes. We propose an information-theoretic interpretation of these results that views cellular multipotency as an instance of maximum entropy statistical inference.

  19. Neural-network hybrid control for antilock braking systems.

    PubMed

    Lin, Chih-Min; Hsu, C F

    2003-01-01

    The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions. PMID:18238018

  20. Remote Neural Pendants In A Welding-Control System

    NASA Technical Reports Server (NTRS)

    Venable, Richard A.; Bucher, Joseph H.

    1995-01-01

    Neural network integrated circuits enhance functionalities of both remote terminals (called "pendants") and communication links, without necessitating installation of additional wires in links. Makes possible to incorporate many features into pendant, including real-time display of critical welding parameters and other process information, capability for communication between technician at pendant and host computer or technician elsewhere in system, and switches and potentiometers through which technician at pendant exerts remote control over such critical aspects of welding process as current, voltage, rate of travel, flow of gas, starting, and stopping. Other potential manufacturing applications include control of spray coating and of curing of composite materials. Potential nonmanufacturing uses include remote control of heating, air conditioning, and lighting in electrically noisy and otherwise hostile environments.

  1. Compound intelligent control system combining fuzzy control with neural networks in a permanent magnetic synchronous motor

    NASA Astrophysics Data System (ADS)

    Zhang, Zhiyuan; Li, Weili; Li, Taifu

    2005-12-01

    An AC motor belongs to the category of a controlled object that is multi-variable, nonlinear and strong correlation, complex to mathematical model, and whose control performance is affected by a time-changing parameter. Therefore, it is very difficult to obtain the desired static and dynamic characteristic through a general fixed regulator. In this paper, the authors present a compound intelligent control strategy, combined with a neural network and fuzzy control. Considering that a neural network is good at self-learning, and a single fuzzy control algorithm is rapid in its response characteristics, the compound control strategy can compensate for a disadvantage of fuzzy control, which is associated with poor stability and precision and also requires solving a puzzle in the time-changing parameters in the controlled object. On the basis of a dynamic model of the permanent magnetic synchronous motor and its working principle, the authors designed the block diagram of a control system, combined a neural PID control and fuzzy control, and studied the corresponding control algorithm in detail. The simulation results show that the compound intelligent control system is good in dynamic performance and robustness.

  2. Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

    NASA Astrophysics Data System (ADS)

    Torghabeh, A. A.; Tousi, A. M.

    2007-08-01

    This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

  3. Autonomic neural control of dynamic cerebral autoregulation in humans

    NASA Technical Reports Server (NTRS)

    Zhang, Rong; Zuckerman, Julie H.; Iwasaki, Kenichi; Wilson, Thad E.; Crandall, Craig G.; Levine, Benjamin D.

    2002-01-01

    BACKGROUND: The purpose of the present study was to determine the role of autonomic neural control of dynamic cerebral autoregulation in humans. METHODS AND RESULTS: We measured arterial pressure and cerebral blood flow (CBF) velocity in 12 healthy subjects (aged 29+/-6 years) before and after ganglion blockade with trimethaphan. CBF velocity was measured in the middle cerebral artery using transcranial Doppler. The magnitude of spontaneous changes in mean blood pressure and CBF velocity were quantified by spectral analysis. The transfer function gain, phase, and coherence between these variables were estimated to quantify dynamic cerebral autoregulation. After ganglion blockade, systolic and pulse pressure decreased significantly by 13% and 26%, respectively. CBF velocity decreased by 6% (P<0.05). In the very low frequency range (0.02 to 0.07 Hz), mean blood pressure variability decreased significantly (by 82%), while CBF velocity variability persisted. Thus, transfer function gain increased by 81%. In addition, the phase lead of CBF velocity to arterial pressure diminished. These changes in transfer function gain and phase persisted despite restoration of arterial pressure by infusion of phenylephrine and normalization of mean blood pressure variability by oscillatory lower body negative pressure. CONCLUSIONS: These data suggest that dynamic cerebral autoregulation is altered by ganglion blockade. We speculate that autonomic neural control of the cerebral circulation is tonically active and likely plays a significant role in the regulation of beat-to-beat CBF in humans.

  4. Geometrical approach to neural net control of movements and posture

    NASA Technical Reports Server (NTRS)

    Pellionisz, A. J.; Ramos, C. F.

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.

  5. Geometrical approach to neural net control of movements and posture.

    PubMed

    Pellionisz, A J; Ramos, C F

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control. PMID:8234751

  6. Differentiation of Multipotent Vascular Stem Cells Contributes to Vascular Diseases

    PubMed Central

    Tang, Zhenyu; Wang, Aijun; Yuan, Falei; Yan, Zhiqiang; Liu, Bo; Chu, Julia S.; Helms, Jill A.

    2012-01-01

    It is generally accepted that the de-differentiation of smooth muscle cells (SMCs) from contractile to proliferative/synthetic phenotype has an important role during vascular remodeling and diseases. Here we provide evidence that challenges this theory. We identify a new type of multipotent vascular stem cell (MVSC) in blood vessel wall. MVSCs express markers including Sox17, Sox10 and S100β, are cloneable, have telomerase activity, and can differentiate into neural cells and mesenchymal stem cell (MSC)-like cells that subsequently differentiate into SMCs. On the other hand, we use lineage tracing with smooth muscle myosin heavy chain as a marker to show that MVSCs and proliferative or synthetic SMCs do not arise from the de-differentiation of mature SMCs. Upon vascular injuries, MVSCs, instead of SMCs, become proliferative, and MVSCs can differentiate into SMCs and chondrogenic cells, thus contributing to vascular remodeling and neointimal hyperplasia. These findings support a new hypothesis that the differentiation of MVSCs, rather than the de-differentiation of SMCs, contributes to vascular remodeling and diseases. PMID:22673902

  7. Expansion of Multipotent Stem Cells from the Adult Human Brain

    PubMed Central

    Murrell, Wayne; Palmero, Emily; Bianco, John; Stangeland, Biljana; Joel, Mrinal; Paulson, Linda; Thiede, Bernd; Grieg, Zanina; Ramsnes, Ingunn; Skjellegrind, Håvard K.; Nygård, Ståle; Brandal, Petter; Sandberg, Cecilie; Vik-Mo, Einar; Palmero, Sheryl; Langmoen, Iver A.

    2013-01-01

    The discovery of stem cells in the adult human brain has revealed new possible scenarios for treatment of the sick or injured brain. Both clinical use of and preclinical research on human adult neural stem cells have, however, been seriously hampered by the fact that it has been impossible to passage these cells more than a very few times and with little expansion of cell numbers. Having explored a number of alternative culturing conditions we here present an efficient method for the establishment and propagation of human brain stem cells from whatever brain tissue samples we have tried. We describe virtually unlimited expansion of an authentic stem cell phenotype. Pluripotency proteins Sox2 and Oct4 are expressed without artificial induction. For the first time multipotency of adult human brain-derived stem cells is demonstrated beyond tissue boundaries. We characterize these cells in detail in vitro including microarray and proteomic approaches. Whilst clarification of these cells’ behavior is ongoing, results so far portend well for the future repair of tissues by transplantation of an adult patient’s own-derived stem cells. PMID:23967194

  8. The adult human brain harbors multipotent perivascular mesenchymal stem cells.

    PubMed

    Paul, Gesine; Özen, Ilknur; Christophersen, Nicolaj S; Reinbothe, Thomas; Bengzon, Johan; Visse, Edward; Jansson, Katarina; Dannaeus, Karin; Henriques-Oliveira, Catarina; Roybon, Laurent; Anisimov, Sergey V; Renström, Erik; Svensson, Mikael; Haegerstrand, Anders; Brundin, Patrik

    2012-01-01

    Blood vessels and adjacent cells form perivascular stem cell niches in adult tissues. In this perivascular niche, a stem cell with mesenchymal characteristics was recently identified in some adult somatic tissues. These cells are pericytes that line the microvasculature, express mesenchymal markers and differentiate into mesodermal lineages but might even have the capacity to generate tissue-specific cell types. Here, we isolated, purified and characterized a previously unrecognized progenitor population from two different regions in the adult human brain, the ventricular wall and the neocortex. We show that these cells co-express markers for mesenchymal stem cells and pericytes in vivo and in vitro, but do not express glial, neuronal progenitor, hematopoietic, endothelial or microglial markers in their native state. Furthermore, we demonstrate at a clonal level that these progenitors have true multilineage potential towards both, the mesodermal and neuroectodermal phenotype. They can be epigenetically induced in vitro into adipocytes, chondroblasts and osteoblasts but also into glial cells and immature neurons. This progenitor population exhibits long-term proliferation, karyotype stability and retention of phenotype and multipotency following extensive propagation. Thus, we provide evidence that the vascular niche in the adult human brain harbors a novel progenitor with multilineage capacity that appears to represent mesenchymal stem cells and is different from any previously described human neural stem cell. Future studies will elucidate whether these cells may play a role for disease or may represent a reservoir that can be exploited in efforts to repair the diseased human brain. PMID:22523602

  9. Output feedback control of a quadrotor UAV using neural networks.

    PubMed

    Dierks, Travis; Jagannathan, Sarangapani

    2010-01-01

    In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture. PMID:19963698

  10. Central neural control of thermoregulation and brown adipose tissue.

    PubMed

    Morrison, Shaun F

    2016-04-01

    Central neural circuits orchestrate the homeostatic repertoire that maintains body temperature during environmental temperature challenges and alters body temperature during the inflammatory response. This review summarizes the experimental underpinnings of our current model of the CNS pathways controlling the principal thermoeffectors for body temperature regulation: cutaneous vasoconstriction controlling heat loss, and shivering and brown adipose tissue for thermogenesis. The activation of these effectors is regulated by parallel but distinct, effector-specific, core efferent pathways within the CNS that share a common peripheral thermal sensory input. Via the lateral parabrachial nucleus, skin thermal afferent input reaches the hypothalamic preoptic area to inhibit warm-sensitive, inhibitory output neurons which control heat production by inhibiting thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to thermogenesis-controlling premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation of spinal circuits necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus sympathetic premotor neurons controlling cutaneous vasoconstriction. The model proposed for central thermoregulatory control provides a useful platform for further understanding of the functional organization of central thermoregulation and elucidating the hypothalamic circuitry and neurotransmitters involved in body temperature regulation. PMID:26924538

  11. Neural time course of emotional conflict control: an ERP study.

    PubMed

    Shen, Yimo; Xue, Song; Wang, Kangcheng; Qiu, Jiang

    2013-04-29

    Previous imaging studies have revealed brain mechanisms associated with emotional conflict control. However, the neural time course remains largely unknown. Therefore, in the present study a face-word Stroop task was used to explore the electrophysiological correlates of emotional conflict control by using event-related potentials (ERPs). Behavioral data indicated that response time of congruent condition was faster than incongruent condition, while the accuracy rates of congruent condition was higher than incongruent condition, which showed a robust emotional conflict effect. ERP revealed N350-550 and P700-800 components in the incongruent minus congruent condition. N350-550 might be related to conflict resolution and response selection; P700-800 might be related to post-response monitoring. PMID:23454616

  12. Neural and electromyographic correlates of wrist posture control.

    PubMed

    Suminski, Aaron J; Rao, Stephen M; Mosier, Kristine M; Scheidt, Robert A

    2007-02-01

    In identical experiments in and out of a MR scanner, we recorded functional magnetic resonance imaging and electromyographic correlates of wrist stabilization against constant and time-varying mechanical perturbations. Positioning errors were greatest while stabilizing random torques. Wrist muscle activity lagged changes in joint angular velocity at latencies suggesting trans-cortical reflex action. Drift in stabilized hand positions gave rise to frequent, accurately directed, corrective movements, suggesting that the brain maintains separate representations of desired wrist angle for feedback control of posture and the generation of discrete corrections. Two patterns of neural activity were evident in the blood-oxygenation-level-dependent (BOLD) time series obtained during stabilization. A cerebello-thalamo-cortical network showed significant activity whenever position errors were present. Here, changes in activation correlated with moment-by-moment changes in position errors (not force), implicating this network in the feedback control of hand position. A second network, showing elevated activity during stabilization whether errors were present or not, included prefrontal cortex, rostral dorsal premotor and supplementary motor area cortices, and inferior aspects of parietal cortex. BOLD activation in some of these regions correlated with positioning errors integrated over a longer time-frame consistent with optimization of feedback performance via adjustment of the behavioral goal (feedback setpoint) and the planning and execution of internally generated motor actions. The finding that nonoverlapping networks demonstrate differential sensitivity to kinematic performance errors over different time scales supports the hypothesis that in stabilizing the hand, the brain recruits distinct neural systems for feedback control of limb position and for evaluation/adjustment of controller parameters in response to persistent errors. PMID:17135464

  13. Neural Control of Rising and Falling Tones in Mandarin Speakers Who Stutter

    ERIC Educational Resources Information Center

    Howell, Peter; Jiang, Jing; Peng, Danling; Lu, Chunming

    2012-01-01

    Neural control of rising and falling tones in Mandarin people who stutter (PWS) was examined by comparing with that which occurs in fluent speakers [Howell, Jiang, Peng, and Lu (2012). Neural control of fundamental frequency rise and fall in Mandarin tones. "Brain and Language, 121"(1), 35-46]. Nine PWS and nine controls were scanned. Functional…

  14. Control of Neural Stem Cell Survival by Electroactive Polymer Substrates

    PubMed Central

    Lundin, Vanessa; Herland, Anna; Berggren, Magnus

    2011-01-01

    Stem cell function is regulated by intrinsic as well as microenvironmental factors, including chemical and mechanical signals. Conducting polymer-based cell culture substrates provide a powerful tool to control both chemical and physical stimuli sensed by stem cells. Here we show that polypyrrole (PPy), a commonly used conducting polymer, can be tailored to modulate survival and maintenance of rat fetal neural stem cells (NSCs). NSCs cultured on PPy substrates containing different counter ions, dodecylbenzenesulfonate (DBS), tosylate (TsO), perchlorate (ClO4) and chloride (Cl), showed a distinct correlation between PPy counter ion and cell viability. Specifically, NSC viability was high on PPy(DBS) but low on PPy containing TsO, ClO4 and Cl. On PPy(DBS), NSC proliferation and differentiation was comparable to standard NSC culture on tissue culture polystyrene. Electrical reduction of PPy(DBS) created a switch for neural stem cell viability, with widespread cell death upon polymer reduction. Coating the PPy(DBS) films with a gel layer composed of a basement membrane matrix efficiently prevented loss of cell viability upon polymer reduction. Here we have defined conditions for the biocompatibility of PPy substrates with NSC culture, critical for the development of devices based on conducting polymers interfacing with NSCs. PMID:21494605

  15. Neural network setpoint control of an advanced test reactor experiment loop simulation

    SciTech Connect

    Cordes, G.A.; Bryan, S.R.; Powell, R.H.; Chick, D.R.

    1990-09-01

    This report describes the design, implementation, and application of artificial neural networks to achieve temperature and flow rate control for a simulation of a typical experiment loop in the Advanced Test Reactor (ATR) located at the Idaho National Engineering Laboratory (INEL). The goal of the project was to research multivariate, nonlinear control using neural networks. A loop simulation code was adapted for the project and used to create a training set and test the neural network controller for comparison with the existing loop controllers. The results for three neural network designs are documented and compared with existing loop controller action. The neural network was shown to be as accurate at loop control as the classical controllers in the operating region represented by the training set. 9 refs., 28 figs., 2 tabs.

  16. Control of Complex Dynamic Systems by Neural Networks

    NASA Technical Reports Server (NTRS)

    Spall, James C.; Cristion, John A.

    1993-01-01

    This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

  17. Multiobjective algebraic synthesis of neural control systems by implicit model following.

    PubMed

    Ferrari, Silvia

    2009-03-01

    The advantages brought about by using classical linear control theory in conjunction with neural approximators have long been recognized in the literature. In particular, using linear controllers to obtain the starting neural control design has been shown to be a key step for the successful development and implementation of adaptive-critic neural controllers. Despite their adaptive capabilities, neural controllers are often criticized for not providing the same performance and stability guarantees as classical linear designs. Therefore, this paper develops an algebraic synthesis procedure for designing dynamic output-feedback neural controllers that are closed-loop stable and meet the same performance objectives as any classical linear design. The performance synthesis problem is addressed by deriving implicit model-following algebraic relationships between model matrices, obtained from the classical design, and the neural control parameters. Additional linear matrix inequalities (LMIs) conditions for closed-loop exponential stability of the neural controller are derived using existing integral quadratic constraints (IQCs) for operators with repeated slope-restricted nonlinearities. The approach is demonstrated by designing a recurrent neural network controller for a highly maneuverable tailfin-controlled missile that meets multiple design objectives, including pole placement for transient tuning, H(infinity) and H(2) performance in the presence of parameter uncertainty, and command-input tracking. PMID:19203887

  18. Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2005-01-01

    This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

  19. Adaptive fuzzy-neural-network control for maglev transportation system.

    PubMed

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies. PMID:18269938

  20. Active Control of Wind-Tunnel Model Aeroelastic Response Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Scott, Robert C.

    2000-01-01

    NASA Langley Research Center, Hampton, VA 23681 Under a joint research and development effort conducted by the National Aeronautics and Space Administration and The Boeing Company (formerly McDonnell Douglas) three neural-network based control systems were developed and tested. The control systems were experimentally evaluated using a transonic wind-tunnel model in the Langley Transonic Dynamics Tunnel. One system used a neural network to schedule flutter suppression control laws, another employed a neural network in a predictive control scheme, and the third employed a neural network in an inverse model control scheme. All three of these control schemes successfully suppressed flutter to or near the limits of the testing apparatus, and represent the first experimental applications of neural networks to flutter suppression. This paper will summarize the findings of this project.

  1. Neural Dynamics of Attentional Cross-Modality Control

    PubMed Central

    Rabinovich, Mikhail; Tristan, Irma; Varona, Pablo

    2013-01-01

    Attentional networks that integrate many cortical and subcortical elements dynamically control mental processes to focus on specific events and make a decision. The resources of attentional processing are finite. Nevertheless, we often face situations in which it is necessary to simultaneously process several modalities, for example, to switch attention between players in a soccer field. Here we use a global brain mode description to build a model of attentional control dynamics. This model is based on sequential information processing stability conditions that are realized through nonsymmetric inhibition in cortical circuits. In particular, we analyze the dynamics of attentional switching and focus in the case of parallel processing of three interacting mental modalities. Using an excitatory-inhibitory network, we investigate how the bifurcations between different attentional control strategies depend on the stimuli and analyze the relationship between the time of attention focus and the strength of the stimuli. We discuss the interplay between attention and decision-making: in this context, a decision-making process is a controllable bifurcation of the attention strategy. We also suggest the dynamical evaluation of attentional resources in neural sequence processing. PMID:23696890

  2. Multipotent pancreas progenitors: Inconclusive but pivotal topic.

    PubMed

    Jiang, Fang-Xu; Morahan, Grant

    2015-12-26

    The establishment of multipotent pancreas progenitors (MPP) should have a significant impact not only on the ontology of the pancreas, but also for the translational research of glucose-responding endocrine β-cells. Deficiency of the latter may lead to the pandemic type 1 or type 2 diabetes mellitus, a metabolic disorder. An ideal treatment of which would potentially be the replacement of destroyed or failed β-cells, by restoring function of endogenous pancreatic endocrine cells or by transplantation of donor islets or in vitro generated insulin-secreting cells. Thus, considerable research efforts have been devoted to identify MPP candidates in the pre- and post-natal pancreas for the endogenous neogenesis or regeneration of endocrine insulin-secreting cells. In order to advance this inconclusive but critical field, we here review the emerging concepts, recent literature and newest developments of potential MPP and propose measures that would assist its forward progression. PMID:26730269

  3. Multipotent pancreas progenitors: Inconclusive but pivotal topic

    PubMed Central

    Jiang, Fang-Xu; Morahan, Grant

    2015-01-01

    The establishment of multipotent pancreas progenitors (MPP) should have a significant impact not only on the ontology of the pancreas, but also for the translational research of glucose-responding endocrine β-cells. Deficiency of the latter may lead to the pandemic type 1 or type 2 diabetes mellitus, a metabolic disorder. An ideal treatment of which would potentially be the replacement of destroyed or failed β-cells, by restoring function of endogenous pancreatic endocrine cells or by transplantation of donor islets or in vitro generated insulin-secreting cells. Thus, considerable research efforts have been devoted to identify MPP candidates in the pre- and post-natal pancreas for the endogenous neogenesis or regeneration of endocrine insulin-secreting cells. In order to advance this inconclusive but critical field, we here review the emerging concepts, recent literature and newest developments of potential MPP and propose measures that would assist its forward progression. PMID:26730269

  4. Robustness of a distributed neural network controller for locomotion in a hexapod robot

    NASA Technical Reports Server (NTRS)

    Chiel, Hillel J.; Beer, Randall D.; Quinn, Roger D.; Espenschied, Kenneth S.

    1992-01-01

    A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

  5. Neural network based adaptive control of nonlinear plants using random search optimization algorithms

    NASA Technical Reports Server (NTRS)

    Boussalis, Dhemetrios; Wang, Shyh J.

    1992-01-01

    This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.

  6. Autonomic neural control of heart rate during dynamic exercise: revisited

    PubMed Central

    White, Daniel W; Raven, Peter B

    2014-01-01

    The accepted model of autonomic control of heart rate (HR) during dynamic exercise indicates that the initial increase is entirely attributable to the withdrawal of parasympathetic nervous system (PSNS) activity and that subsequent increases in HR are entirely attributable to increases in cardiac sympathetic activity. In the present review, we sought to re-evaluate the model of autonomic neural control of HR in humans during progressive increases in dynamic exercise workload. We analysed data from both new and previously published studies involving baroreflex stimulation and pharmacological blockade of the autonomic nervous system. Results indicate that the PSNS remains functionally active throughout exercise and that increases in HR from rest to maximal exercise result from an increasing workload-related transition from a 4 : 1 vagal–sympathetic balance to a 4 : 1 sympatho–vagal balance. Furthermore, the beat-to-beat autonomic reflex control of HR was found to be dependent on the ability of the PSNS to modulate the HR as it was progressively restrained by increasing workload-related sympathetic nerve activity. In conclusion: (i) increases in exercise workload-related HR are not caused by a total withdrawal of the PSNS followed by an increase in sympathetic tone; (ii) reciprocal antagonism is key to the transition from vagal to sympathetic dominance, and (iii) resetting of the arterial baroreflex causes immediate exercise-onset reflexive increases in HR, which are parasympathetically mediated, followed by slower increases in sympathetic tone as workloads are increased. PMID:24756637

  7. Chemo-mechanical control of neural stem cell differentiation

    NASA Astrophysics Data System (ADS)

    Geishecker, Emily R.

    Cellular processes such as adhesion, proliferation, and differentiation are controlled in part by cell interactions with the microenvironment. Cells can sense and respond to a variety of stimuli, including soluble and insoluble factors (such as proteins and small molecules) and externally applied mechanical stresses. Mechanical properties of the environment, such as substrate stiffness, have also been suggested to play an important role in cell processes. The roles of both biochemical and mechanical signaling in fate modification of stem cells have been explored independently. However, very few studies have been performed to study well-controlled chemo-mechanotransduction. The objective of this work is to design, synthesize, and characterize a chemo-mechanical substrate to encourage neuronal differentiation of C17.2 neural stem cells. In Chapter 2, Polyacrylamide (PA) gels of varying stiffnesses are functionalized with differing amounts of whole collagen to investigate the role of protein concentration in combination with substrate stiffness. As expected, neurons on the softest substrate were more in number and neuronal morphology than those on stiffer substrates. Neurons appeared locally aligned with an expansive network of neurites. Additional experiments would allow for statistical analysis to determine if and how collagen density impacts C17.2 differentiation in combination with substrate stiffness. Due to difficulties associated with whole protein approaches, a similar platform was developed using mixed adhesive peptides, derived from fibronectin and laminin, and is presented in Chapter 3. The matrix elasticity and peptide concentration can be individually modulated to systematically probe the effects of chemo-mechanical signaling on differentiation of C17.2 cells. Polyacrylamide gel stiffness was confirmed using rheological techniques and found to support values published by Yeung et al. [1]. Cellular growth and differentiation were assessed by cell counts

  8. Neural stem cells: Brain building blocks and beyond

    PubMed Central

    Bergström, Tobias

    2012-01-01

    Neural stem cells are the origins of neurons and glia and generate all the differentiated neural cells of the mammalian central nervous system via the formation of intermediate precursors. Although less frequent, neural stem cells persevere in the postnatal brain where they generate neurons and glia. Adult neurogenesis occurs throughout life in a few limited brain regions. Regulation of neural stem cell number during central nervous system development and in adult life is associated with rigorous control. Failure in this regulation may lead to e.g. brain malformation, impaired learning and memory, or tumor development. Signaling pathways that are perturbed in glioma are the same that are important for neural stem cell self-renewal, differentiation, survival, and migration. The heterogeneity of human gliomas has impeded efficient treatment, but detailed molecular characterization together with novel stem cell-like glioma cell models that reflect the original tumor gives opportunities for research into new therapies. The observation that neural stem cells can be isolated and expanded in vitro has opened new avenues for medical research, with the hope that they could be used to compensate the loss of cells that features in several severe neurological diseases. Multipotent neural stem cells can be isolated from the embryonic and adult brain and maintained in culture in a defined medium. In addition, neural stem cells can be derived from embryonic stem cells and induced pluripotent stem cells by in vitro differentiation, thus adding to available models to study stem cells in health and disease. PMID:22512245

  9. Neural Controller Design-Based Adaptive Control for Nonlinear MIMO Systems With Unknown Hysteresis Inputs.

    PubMed

    Liu, Yan-Jun; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

    2016-01-01

    This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems in interconnected form. The studied systems are composed of N subsystems in pure feedback structure and the interconnection terms are contained in every equation of each subsystem. Moreover, the studied systems consider the effects of Prandtl-Ishlinskii (PI) hysteresis model. It is for the first time to study the control problem for such a class of systems. In addition, the proposed scheme removes an important assumption imposed on the previous works that the bounds of the parameters in PI hysteresis are known. The radial basis functions neural networks are employed to approximate unknown functions. The adaptation laws and the controllers are designed by employing the backstepping technique. The closed-loop system can be proven to be stable by using Lyapunov theorem. A simulation example is studied to validate the effectiveness of the scheme. PMID:25898325

  10. Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

    PubMed

    Xia, Kewei; Huo, Wei

    2016-05-01

    This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. PMID:26892402

  11. Volitional control of neural activity: implications for brain–computer interfaces

    PubMed Central

    Fetz, Eberhard E

    2007-01-01

    Successful operation of brain–computer interfaces (BCI) and brain–machine interfaces (BMI) depends significantly on the degree to which neural activity can be volitionally controlled. This paper reviews evidence for such volitional control in a variety of neural signals, with particular emphasis on the activity of cortical neurons. Some evidence comes from conventional experiments that reveal volitional modulation in neural activity related to behaviours, including real and imagined movements, cognitive imagery and shifts of attention. More direct evidence comes from studies on operant conditioning of neural activity using biofeedback, and from BCI/BMI studies in which neural activity controls cursors or peripheral devices. Limits in the degree of accuracy of control in the latter studies can be attributed to several possible factors. Some of these factors, particularly limited practice time, can be addressed with long-term implanted BCIs. Preliminary observations with implanted circuits implementing recurrent BCIs are summarized. PMID:17234689

  12. Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control

    NASA Astrophysics Data System (ADS)

    Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

    2016-04-01

    In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.

  13. Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control.

    PubMed

    Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

    2016-04-01

    In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained. PMID:27131492

  14. Neural network based optimal control of HVAC&R systems

    NASA Astrophysics Data System (ADS)

    Ning, Min

    Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the

  15. Central chemoreceptors and neural mechanisms of cardiorespiratory control.

    PubMed

    Moreira, T S; Takakura, A C; Damasceno, R S; Falquetto, B; Totola, L T; Sobrinho, C R; Ragioto, D T; Zolezi, F P

    2011-09-01

    The arterial partial pressure (P(CO)(2)) of carbon dioxide is virtually constant because of the close match between the metabolic production of this gas and its excretion via breathing. Blood gas homeostasis does not rely solely on changes in lung ventilation, but also to a considerable extent on circulatory adjustments that regulate the transport of CO(2) from its sites of production to the lungs. The neural mechanisms that coordinate circulatory and ventilatory changes to achieve blood gas homeostasis are the subject of this review. Emphasis will be placed on the control of sympathetic outflow by central chemoreceptors. High levels of CO(2) exert an excitatory effect on sympathetic outflow that is mediated by specialized chemoreceptors such as the neurons located in the retrotrapezoid region. In addition, high CO(2) causes an aversive awareness in conscious animals, activating wake-promoting pathways such as the noradrenergic neurons. These neuronal groups, which may also be directly activated by brain acidification, have projections that contribute to the CO(2)-induced rise in breathing and sympathetic outflow. However, since the level of activity of the retrotrapezoid nucleus is regulated by converging inputs from wake-promoting systems, behavior-specific inputs from higher centers and by chemical drive, the main focus of the present manuscript is to review the contribution of central chemoreceptors to the control of autonomic and respiratory mechanisms. PMID:21789465

  16. Discrete-time neural inverse optimal control for nonlinear systems via passivation.

    PubMed

    Ornelas-Tellez, Fernando; Sanchez, Edgar N; Loukianov, Alexander G

    2012-08-01

    This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot. PMID:24807528

  17. Neural network based approach for tuning of SNS feedback and feedforward controllers.

    SciTech Connect

    Kwon, S. I.; Prokop, M. S.; Regan, A. H.

    2002-01-01

    The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

  18. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

    PubMed

    Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

  19. Lineage-instructive function of C/EBPα in multipotent hematopoietic cells and early thymic progenitors.

    PubMed

    Wölfler, Albert; Danen-van Oorschot, Astrid A; Haanstra, Jurgen R; Valkhof, Marijke; Bodner, Claudia; Vroegindeweij, Eric; van Strien, Paulette; Novak, Alexandra; Cupedo, Tom; Touw, Ivo P

    2010-11-18

    Hematopoiesis is tightly controlled by transcription regulatory networks, but how and when specific transcription factors control lineage commitment are still largely unknown. Within the hematopoietic stem cell (Lin(-)Sca-1(+)c-Kit(+)) compartment these lineage-specific transcription factors are expressed at low levels but are up-regulated with the process of lineage specification. CCAAT/enhancer binding protein α (C/EBPα) represents one of these factors and is involved in myeloid development and indispensable for formation of granulocytes. To track the cellular fate of stem and progenitor cells, which express C/EBPα, we developed a mouse model expressing Cre recombinase from the Cebpa promoter and a conditional EYFP allele. We show that Cebpa/EYFP(+) cells represent a significant subset of multipotent hematopoietic progenitors, which predominantly give rise to myeloid cells in steady-state hematopoiesis. C/EBPα induced a strong myeloid gene expression signature and down-regulated E2A-induced regulators of early lymphoid development. In addition, Cebpa/EYFP(+) cells compose a fraction of early thymic progenitors with robust myeloid potential. However, Cebpa/EYFP(+) multipotent hematopoietic progenitors and early thymic progenitors retained the ability to develop into erythroid and T-lymphoid lineages, respectively. These findings support an instructive but argue against a lineage-restrictive role of C/EBPα in multipotent hematopoietic and thymic progenitors. PMID:20807890

  20. Path optimisation of a mobile robot using an artificial neural network controller

    NASA Astrophysics Data System (ADS)

    Singh, M. K.; Parhi, D. R.

    2011-01-01

    This article proposed a novel approach for design of an intelligent controller for an autonomous mobile robot using a multilayer feed forward neural network, which enables the robot to navigate in a real world dynamic environment. The inputs to the proposed neural controller consist of left, right and front obstacle distance with respect to its position and target angle. The output of the neural network is steering angle. A four layer neural network has been designed to solve the path and time optimisation problem of mobile robots, which deals with the cognitive tasks such as learning, adaptation, generalisation and optimisation. A back propagation algorithm is used to train the network. This article also analyses the kinematic design of mobile robots for dynamic movements. The simulation results are compared with experimental results, which are satisfactory and show very good agreement. The training of the neural nets and the control performance analysis has been done in a real experimental setup.

  1. Real-time neural network based camera localization and its extension to mobile robot control.

    PubMed

    Choi, D H; Oh, S Y

    1997-06-01

    The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models. PMID:9427102

  2. Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

    SciTech Connect

    Gu, Z.; Rizy, D.T.

    1996-10-01

    A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks and a nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

  3. Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

    SciTech Connect

    Gu, Z.; Rizy, D.T.

    1996-02-01

    A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

  4. Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2005-01-01

    This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example

  5. Translating Principles of Neural Plasticity into Research on Speech Motor Control Recovery and Rehabilitation

    ERIC Educational Resources Information Center

    Ludlow, Christy L.; Hoit, Jeannette; Kent, Raymond; Ramig, Lorraine O.; Shrivastav, Rahul; Strand, Edythe; Yorkston, Kathryn; Sapienza, Christine M.

    2008-01-01

    Purpose: To review the principles of neural plasticity and make recommendations for research on the neural bases for rehabilitation of neurogenic speech disorders. Method: A working group in speech motor control and disorders developed this report, which examines the potential relevance of basic research on the brain mechanisms involved in neural…

  6. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.

    PubMed

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

  7. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

    PubMed Central

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

  8. Control of neural crest cell dispersion in the trunk of the avian embryo.

    PubMed

    Erickson, C A

    1985-09-01

    Many hypotheses have been advanced to explain the orientation and directional migration of neural crest cells. These include positive and negative chemotaxis, haptotaxis, galvanotaxis, and contact inhibition. To test directly the factors that may control the directional dispersion of the neural crest, I have employed a variety of grafting techniques in living embryos. In addition, time-lapse video microscopy has been used to study neural crest cells in tissue culture. Trunk neural crest cells normally disperse from their origin at the dorsal neural tube along two extracellular pathways. One pathway extends laterally between the ectoderm and somites. When either pigmented neural crest cells or neural crest cells isolated from 24-hr cultures are grafted into the space lateral to the somites, they migrate: (1) medially toward the neural tube in the space between the ectoderm and somites and (2) ventrally along intersomitic blood vessels. Once the grafted cells contact the posterior cardinal vein and dorsal aorta they migrate along both blood vessels for several somite lengths in the anterior-posterior axis. Neural crest cells grafted lateral to the somites do not immediately move laterally into the somatic mesoderm of the body wall or the limb. Dispersion of neural crest cells into the mesoderm occurs only after blood vessels and nerves have first invaded, which the grafted cells then follow. The other neural crest pathway extends ventrally alongside the neural tube in the intersomitic space. When neural crest cells were grafted to a ventral position, between the notochord and dorsal aorta, in this intersomitic pathway at the axial level of the last somite, the grafted cells migrate rapidly within 2 hr in two directions: (1) dorsally, in the intersomitic space, until the grafted cells contact the ventrally moving stream of the host neural crest and (2) laterally, along the dorsal aorta and endoderm. All of the above experiments indicate that neither a preestablished

  9. Neural mechanisms of timing control in a coincident timing task.

    PubMed

    Masaki, Hiroaki; Sommer, Werner; Takasawa, Noriyoshi; Yamazaki, Katuo

    2012-04-01

    Many ball sports such as tennis or baseball require precise temporal anticipation of both sensory input and motor output (i.e., receptor anticipation and effector anticipation, respectively) and close performance monitoring. We investigated the neural mechanisms underlying timing control and performance monitoring in a coincident timing task involving both types of anticipations. Peak force for two time-to-peak force (TTP) conditions-recorded with a force-sensitive key-was required to coincide with a specific position of a stimulus rotating either slow or fast on a clock face while the contingent negative variation (CNV) and the motor-elicited negativity were recorded. Absolute timing error was generally smaller for short TTP (high velocity) conditions. CNV amplitudes increased with both faster stimulus velocity and longer TTPs possibly reflecting increased motor programming efforts. In addition, the motor-elicited negativity was largest in the slow stimulus/short TTP condition, probably representing some forms of performance monitoring as well as shorter response duration. Our findings indicate that the coincident timing task is a good model for real-life situations of tool use. PMID:22415201

  10. Brain lipid sensing and the neural control of energy balance.

    PubMed

    Magnan, Christophe; Levin, Barry E; Luquet, Serge

    2015-12-15

    Fatty acid (FA) -sensitive neurons are present in the brain, especially the hypothalamus, and play a key role in the neural control of energy and glucose homeostasis including feeding behavior, secretion insulin and action. Subpopulations of neurons in the arcuate and ventromedial hypothalamic nuclei are selectively either activated or inhibited by FA. Molecular effectors of these FA effects include ion channels such as chloride, potassium or calcium. In addition, at least half of the responses in the hypothalamic ventromedial FA neurons are mediated through interaction with the FA translocator/receptor, FAT/CD36, that does not require metabolism to activate intracellular signaling downstream. Recently, an important role of lipoprotein lipase in FA detection has also been demonstrated not only in the hypothalamus, but also in the hippocampus and striatum. Finally, FA could overload energy homeostasis via increased hypothalamic ceramide synthesis which could, in turn, contribute to the pathogenesis of diabetes of obesity and/or type 2 in predisposed individuals by disrupting the endocrine signaling pathways of insulin and/or leptin. PMID:26415589

  11. Decoding Neural Circuits that Control Compulsive Sucrose-Seeking

    PubMed Central

    Nieh, Edward H.; Matthews, Gillian A.; Allsop, Stephen A.; Presbrey, Kara N.; Leppla, Christopher A.; Wichmann, Romy; Neve, Rachael; Wildes, Craig P.; Tye, Kay M.

    2015-01-01

    The lateral hypothalamic (LH) projection to the ventral tegmental area (VTA) has been linked to reward processing, but the computations within the LH-VTA loop that give rise to specific aspects of behavior have been difficult to isolate. We show that LH-VTA neurons encode the learned action of seeking a reward, independent of reward availability. In contrast, LH neurons downstream of VTA encode reward-predictive cues and unexpected reward omission. We show that inhibiting the LH-VTA pathway reduces “compulsive” sucrose-seeking, but not food consumption in hungry mice. We reveal that the LH sends excitatory and inhibitory input onto VTA dopamine (DA) and GABA neurons, and that the GABAergic projection drives feeding-related behavior. Our study overlays information about the type, function and connectivity of LH neurons and identifies a neural circuit that selectively controls compulsive sugar consumption, without preventing feeding necessary for survival, providing a potential target for therapeutic interventions for compulsive-overeating disorder. PMID:25635460

  12. Gastrointestinal Parasites and the Neural Control of Gut Functions

    PubMed Central

    Halliez, Marie C. M.; Buret, André G.

    2015-01-01

    Gastrointestinal motility and transport of water and electrolytes play key roles in the pathophysiology of diarrhea upon exposure to enteric parasites. These processes are actively modulated by the enteric nervous system (ENS), which includes efferent, and afferent neurons, as well as interneurons. ENS integrity is essential to the maintenance of homeostatic gut responses. A number of gastrointestinal parasites are known to cause disease by altering the ENS. The mechanisms remain incompletely understood. Cryptosporidium parvum, Giardia duodenalis (syn. Giardia intestinalis, Giardia lamblia), Trypanosoma cruzi, Schistosoma species and others alter gastrointestinal motility, absorption, or secretion at least in part via effects on the ENS. Recent findings also implicate enteric parasites such as C. parvum and G. duodenalis in the development of post-infectious complications such as irritable bowel syndrome, which further underscores their effects on the gut-brain axis. This article critically reviews recent advances and the current state of knowledge on the impact of enteric parasitism on the neural control of gut functions, and provides insights into mechanisms underlying these abnormalities. PMID:26635531

  13. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.

    PubMed

    Yuan, Junjia; Meng, Hao; Zhu, Qidan; Zhou, Jiajia

    2016-01-01

    The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method. PMID:27293422

  14. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method

    PubMed Central

    Yuan, Junjia; Meng, Hao; Zhu, Qidan; Zhou, Jiajia

    2016-01-01

    The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method. PMID:27293422

  15. Experiments in Neural-Network Control of a Free-Flying Space Robot

    NASA Technical Reports Server (NTRS)

    Wilson, Edward

    1995-01-01

    Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

  16. Communications and control for electric power systems: Power system stability applications of artificial neural networks

    NASA Technical Reports Server (NTRS)

    Toomarian, N.; Kirkham, Harold

    1994-01-01

    This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

  17. Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill

    NASA Astrophysics Data System (ADS)

    Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.

    2008-06-01

    The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.

  18. The optimization of force inputs for active structural acoustic control using a neural network

    NASA Technical Reports Server (NTRS)

    Cabell, R. H.; Lester, H. C.; Silcox, R. J.

    1992-01-01

    This paper investigates the use of a neural network to determine which force actuators, of a multi-actuator array, are best activated in order to achieve structural-acoustic control. The concept is demonstrated using a cylinder/cavity model on which the control forces, produced by piezoelectric actuators, are applied with the objective of reducing the interior noise. A two-layer neural network is employed and the back propagation solution is compared with the results calculated by a conventional, least-squares optimization analysis. The ability of the neural network to accurately and efficiently control actuator activation for interior noise reduction is demonstrated.

  19. Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill

    SciTech Connect

    Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.

    2008-06-12

    The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.

  20. The Relationship of Aluminium and Silver to Neural Tube Defects; a Case Control

    PubMed Central

    Ramírez-Altamirano, María de Jesús; Fenton-Navarro, Patricia; Sivet-Chiñas, Elvira; Harp-Iturribarria, Flor de María; Martínez-Cruz, Ruth; Cruz, Pedro Hernández; Cruz, Margarito Martínez; Pérez-Campos, Eduardo

    2012-01-01

    Objective The purpose of this study was to identify the relationship of neurotoxic inorganic elements in the hair of patients with the diagnosis of Neural Tube Defects. Our initial hypothesis was that neurotoxic inorganic elements were associated with Neural Tube Defects. Methods Twenty-three samples of hair from newborns were obtained from the General Hospital, “Aurelio Valdivieso” in the city of Oaxaca, Mexico. The study group included 8 newborn infants with neural tube pathology. The control group was composed of 15 newborns without this pathology. The presence of inorganic elements in the hair samples was determined by inductively-coupled plasma spectroscopy (spectroscopic emission of the plasma). Findings The population of newborns with Neural Tube Defects showed significantly higher values of the following elements than the control group: Aluminium, Neural Tube Defects 152.77±51.06 µg/g, control group 76.24±27.89 µg/g; Silver, Neural Tube Defects 1.45±0.76, control group 0.25±0.53 µg/g; Potassium, Neural Tube Defects 553.87±77.91 µg/g, control group 341.13±205.90 µg/g. Association was found at 75 percentile between aluminium plus silver, aluminium plus potassium, silver plus potassium, and potassium plus sodium. Conclusion In the hair of newborns with Neural Tube Defects, the following metals were increased: aluminium, silver. Given the neurotoxicity of the same, and association of Neural Tube Defects with aluminum and silver, one may infer that they may be participating as factors in the development of Neural Tube Defects. PMID:23400307

  1. An approximate internal model-based neural control for unknown nonlinear discrete processes.

    PubMed

    Li, Han-Xiong; Deng, Hua

    2006-05-01

    An approximate internal model-based neural control (AIMNC) strategy is proposed for unknown nonaffine nonlinear discrete processes under disturbed environment. The proposed control strategy has some clear advantages in respect to existing neural internal model control methods. It can be used for open-loop unstable nonlinear processes or a class of systems with unstable zero dynamics. Based on a novel input-output approximation, the proposed neural control law can be derived directly and implemented straightforward for an unknown process. Only one neural network needs to be trained and control algorithm can be directly obtained from model identification without further training. The stability and robustness of a closed-loop system can be derived analytically. Extensive simulations demonstrate the superior performance of the proposed AIMNC strategy. PMID:16722170

  2. Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

    PubMed

    Long, Lijun; Zhao, Jun

    2015-07-01

    This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed. PMID:25122844

  3. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    NASA Astrophysics Data System (ADS)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

  4. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    PubMed

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. PMID:21788017

  5. Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

    PubMed

    Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

    2015-11-01

    This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. PMID:26506019

  6. Feedback-linearization-based neural adaptive control for unknown nonaffine nonlinear discrete-time systems.

    PubMed

    Deng, Hua; Li, Han-Xiong; Wu, Yi-Hu

    2008-09-01

    A new feedback-linearization-based neural network (NN) adaptive control is proposed for unknown nonaffine nonlinear discrete-time systems. An equivalent model in affine-like form is first derived for the original nonaffine discrete-time systems as feedback linearization methods cannot be implemented for such systems. Then, feedback linearization adaptive control is implemented based on the affine-like equivalent model identified with neural networks. Pretraining is not required and the weights of the neural networks used in adaptive control are directly updated online based on the input-output measurement. The dead-zone technique is used to remove the requirement of persistence excitation during the adaptation. With the proposed neural network adaptive control, stability and performance of the closed-loop system are rigorously established. Illustrated examples are provided to validate the theoretical findings. PMID:18779092

  7. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    SciTech Connect

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  8. Application of backpropagation neural architectures to the realization of control transfer functions and compensators

    NASA Astrophysics Data System (ADS)

    Diaz-Robainas, Regino R.; Pandya, Abhijit S.; Huang, Ming Z.

    1994-03-01

    A method is developed to design simulations of neural-network based transfer functions, applicable to both linear and nonlinear structures. The algorithm used to implement the trainable neural mechanism is backpropagation. Using the trained structures as building blocks, a neural architecture is constructed in order to drive systems from expected inputs to satisfactory transient and steady-state output performance, in effect, the scope of control compensation; this method results in the design of neural-net control compensators. The algorithms are coded in a PC-based prolog, traditionally used for rule-based logic and Artificial Intelligence, rather than for Neural or Fuzzy models. Given a sequence representing the time-sample of a desired control input trajectory that will drive the plant to a desired output response, such a control input will be modelled as the desired output layer of an antecedent network driven by an error vector consistent with the closed-loop system's commanded behavior. This Controller network is trained to provide such an output profile for all expected inputs, in accordance with arbitrary specifications of rise-time, permitted overshoot, settling time, etc. The control vectors are generated as a by-product of this training. Additionally, a correlation is investigated between classical control parameters and the characteristics of the weight matrices, threshold vectors, and representation traits of the converged neural nets.

  9. Finite-time synchronization control of a class of memristor-based recurrent neural networks.

    PubMed

    Jiang, Minghui; Wang, Shuangtao; Mei, Jun; Shen, Yanjun

    2015-03-01

    This paper presents a global and local finite-time synchronization control law for memristor neural networks. By utilizing the drive-response concept, differential inclusions theory, and Lyapunov functional method, we establish several sufficient conditions for finite-time synchronization between the master and corresponding slave memristor-based neural network with the designed controller. In comparison with the existing results, the proposed stability conditions are new, and the obtained results extend some previous works on conventional recurrent neural networks. Two numerical examples are provided to illustrate the effective of the design method. PMID:25536233

  10. Experimental investigation of active vibration control using neural networks and piezoelectric actuators

    NASA Astrophysics Data System (ADS)

    Jha, Ratneshwar; Rower, Jacob

    2002-02-01

    The use of neural networks for identification and control of smart structures is investigated experimentally. Piezoelectric actuators are employed to suppress the vibrations of a cantilevered plate subject to impulse, sine wave and band-limited white noise disturbances. The neural networks used are multilayer perceptrons trained with error backpropagation. Validation studies show that the identifier predicts the system dynamics accurately. The controller is trained adaptively with the help of the neural identifier. Experimental results demonstrate excellent closed-loop performance and robustness of the neurocontroller.

  11. Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Williams-Hayes, Peggy; Kaneshige, John T.; Stachowiak, Susan J.

    2006-01-01

    Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

  12. Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

    2006-01-01

    Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

  13. Method for neural network control of motion using real-time environmental feedback

    NASA Technical Reports Server (NTRS)

    Buckley, Theresa M. (Inventor)

    1997-01-01

    A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

  14. Amniotic fluid as a source of multipotent cells for clinical use.

    PubMed

    Young, Bruce K; Chan, Michael K; Liu, Li; Basch, Ross S

    2016-04-01

    Amniotic fluid cells (AFC) from 2nd trimester amniocentesis have been found to be a source of multipotent stem cells which might overcome the limitations of expansion, histocompatibility, tumorigenesis, and ethical issues associated with using human embryonic cells, umbilical cord, cord blood, bone marrow, and induced pluripotent cells. Previous work by our group and others demonstrated multipotency and the ability to grow well in culture. However, all these studies were done in media containing fetal calf serum. We sought to observe the properties of AFC grown in serum-free media as that would be required for clinical transplantation in humans. Fresh samples were obtained from three patients, and each sample divided into a culture whose cells were not exposed to fetal calf serum, and the other half into a standard culture medium containing fetal calf serum. Doubling time and stem cell marker expression by flow cytometry were assessed. Differentiation to neural, osteoid, and chondrogenic lineages was induced using appropriate media and confirmed by fluorescent microscopy, histology, and immunohistochemistry. There were no statistically significant differences between cells grown serum-free and in standard media in any of these parameters. The data supports the possibility of clinical use of AFC in stem cell transplantation. PMID:26115489

  15. Isolation and characterization of multipotent cells from human fetal dermis.

    PubMed

    Chinnici, Cinzia Maria; Amico, Giandomenico; Monti, Marcello; Motta, Stefania; Casalone, Rosario; Petri, Sergio Li; Spada, Marco; Gridelli, Bruno; Conaldi, Pier Giulio

    2014-01-01

    We report that cells from human fetal dermis, termed here multipotent fetal dermal cells, can be isolated with high efficiency by using a nonenzymatic, cell outgrowth method. The resulting cell population was consistent with the definition of mesenchymal stromal cells by the International Society for Cellular Therapy. As multipotent fetal dermal cells proliferate extensively, with no loss of multilineage differentiation potential up to passage 25, they may be an ideal source for cell therapy to repair damaged tissues and organs. Multipotent fetal dermal cells were not recognized as targets by T lymphocytes in vitro, thus supporting their feasibility for allogenic transplantation. Moreover, the expansion protocol did not affect the normal phenotype and karyotype of cells. When compared with adult dermal cells, fetal cells displayed several advantages, including a greater cellular yield after isolation, the ability to proliferate longer, and the retention of differentiation potential. Interestingly, multipotent fetal dermal cells expressed the pluripotency marker SSEA4 (90.56 ± 3.15% fetal vs. 10.5 ± 8.5% adult) and coexpressed mesenchymal and epithelial markers (>80% CD90(+)/CK18(+) cells), coexpression lacking in the adult counterparts isolated under the same conditions. Multipotent fetal dermal cells were able to form capillary structures, as well as differentiate into a simple epithelium in vitro, indicating skin regeneration capabilities. PMID:23768775

  16. A Wirelessly Powered and Controlled Device for Optical Neural Control of Freely-Behaving Animals

    PubMed Central

    Wentz, Christian T.; Bernstein, Jacob G.; Monahan, Patrick; Guerra, Alexander; Rodriguez, Alex; Boyden, Edward S.

    2011-01-01

    Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists’ capacity to understand how defined neural circuit elements contribute to normal and pathological brain functions. Typically awake behaving experiments are conducted by inserting an optical fiber into the brain, tethered to a remote laser, or by utilizing an implanted LED, tethered to a remote power source. A fully wireless system would enable chronic or longitudinal experiments where long duration tethering is impractical, and would also support high-throughput experimentation. However, the high power requirements of light sources (LEDs, lasers), especially in the context of the high-frequency pulse trains often desired in experiments, precludes battery-powered approaches from being widely applicable. We have developed a headborne device weighing 2 grams capable of wirelessly receiving power using a resonant RF power link and storing the energy in an adaptive supercapacitor circuit, which can algorithmically control one or more headborne LEDs via a microcontroller. The device can deliver approximately 2W of power to the LEDs in steady state, and 4.3W in bursts. We also present an optional radio transceiver module (1 gram) which, when added to the base headborne device, enables real-time updating of light delivery protocols; dozens of devices can be simultaneously controlled from one computer. We demonstrate use of the technology to wirelessly drive cortical control of movement in mice. These devices may serve as prototypes for clinical ultra-precise neural prosthetics that use light as the modality of biological control. PMID:21701058

  17. A wirelessly powered and controlled device for optical neural control of freely-behaving animals

    NASA Astrophysics Data System (ADS)

    Wentz, Christian T.; Bernstein, Jacob G.; Monahan, Patrick; Guerra, Alexander; Rodriguez, Alex; Boyden, Edward S.

    2011-08-01

    Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists' capacity to understand how defined neural circuit elements contribute to normal and pathological brain functions. Typically, awake behaving experiments are conducted by inserting an optical fiber into the brain, tethered to a remote laser, or by utilizing an implanted light-emitting diode (LED), tethered to a remote power source. A fully wireless system would enable chronic or longitudinal experiments where long duration tethering is impractical, and would also support high-throughput experimentation. However, the high power requirements of light sources (LEDs, lasers), especially in the context of the extended illumination periods often desired in experiments, precludes battery-powered approaches from being widely applicable. We have developed a headborne device weighing 2 g capable of wirelessly receiving power using a resonant RF power link and storing the energy in an adaptive supercapacitor circuit, which can algorithmically control one or more headborne LEDs via a microcontroller. The device can deliver approximately 2 W of power to the LEDs in steady state, and 4.3 W in bursts. We also present an optional radio transceiver module (1 g) which, when added to the base headborne device, enables real-time updating of light delivery protocols; dozens of devices can be controlled simultaneously from one computer. We demonstrate use of the technology to wirelessly drive cortical control of movement in mice. These devices may serve as prototypes for clinical ultra-precise neural prosthetics that use light as the modality of biological control.

  18. Control of Wind Tunnel Operations Using Neural Net Interpretation of Flow Visualization Records

    NASA Technical Reports Server (NTRS)

    Buggele, Alvin E.; Decker, Arthur J.

    1994-01-01

    Neural net control of operations in a small subsonic/transonic/supersonic wind tunnel at Lewis Research Center is discussed. The tunnel and the layout for neural net control or control by other parallel processing techniques are described. The tunnel is an affordable, multiuser platform for testing instrumentation and components, as well as parallel processing and control strategies. Neural nets have already been tested on archival schlieren and holographic visualizations from this tunnel as well as recent supersonic and transonic shadowgraph. This paper discusses the performance of neural nets for interpreting shadowgraph images in connection with a recent exercise for tuning the tunnel in a subsonic/transonic cascade mode of operation. That mode was operated for performing wake surveys in connection with NASA's Advanced Subsonic Technology (AST) noise reduction program. The shadowgraph was presented to the neural nets as 60 by 60 pixel arrays. The outputs were tunnel parameters such as valve settings or tunnel state identifiers for selected tunnel operating points, conditions, or states. The neural nets were very sensitive, perhaps too sensitive, to shadowgraph pattern detail. However, the nets exhibited good immunity to variations in brightness, to noise, and to changes in contrast. The nets are fast enough so that ten or more can be combined per control operation to interpret flow visualization data, point sensor data, and model calculations. The pattern sensitivity of the nets will be utilized and tested to control wind tunnel operations at Mach 2.0 based on shock wave patterns.

  19. SIRT1 and Neural Cell Fate Determination.

    PubMed

    Cai, Yulong; Xu, Le; Xu, Haiwei; Fan, Xiaotang

    2016-07-01

    During the development of the central nervous system (CNS), neurons and glia are derived from multipotent neural stem cells (NSCs) undergoing self-renewal. NSC commitment and differentiation are tightly controlled by intrinsic and external regulatory mechanisms in space- and time-related fashions. SIRT1, a silent information regulator 2 (Sir2) ortholog, is expressed in several areas of the brain and has been reported to be involved in the self-renewal, multipotency, and fate determination of NSCs. Recent studies have highlighted the role of the deacetylase activity of SIRT1 in the determination of the final fate of NSCs. This review summarizes the roles of SIRT1 in the expansion and differentiation of NSCs, specification of neuronal subtypes and glial cells, and reprogramming of functional neurons from embryonic stem cells and fibroblasts. This review also discusses potential signaling pathways through which SIRT1 can exhibit versatile functions in NSCs to regulate the cell fate decisions of neurons and glia. PMID:25850787

  20. Nonlinear adaptive control systems design of BTT missile based on fully tuned RBF neural networks

    NASA Astrophysics Data System (ADS)

    Hu, Yunan; Jin, Yuqiang; Li, Jing

    2003-09-01

    Based on fully tuned RBF neural networks and backstepping control techniques, a novel nonlinear adaptive control scheme is proposed for missile control systems with a general set of uncertainties. The effect of the uncertainties is synthesized one term in the design procedure. Then RBF neural networks are used to eliminate its effect. The nonlinear adaptive controller is designed using backstepping control techniques. The control problem is resolved while the control coefficient matrix is unknown. The adaptive tuning rules for updating all of the parameters of the fully tuned RBF neural networks are firstly derived by the Lyapunov stability theorem. Finally, nonlinear 6-DOF numerical simulation results for a BTT missile model are presented to demonstrate the effectiveness of the proposed method.

  1. Neural self-tuning adaptive control of non-minimum phase system

    NASA Technical Reports Server (NTRS)

    Ho, Long T.; Bialasiewicz, Jan T.; Ho, Hai T.

    1993-01-01

    The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

  2. Neural Control of the Cardiovascular System in Space

    NASA Technical Reports Server (NTRS)

    Levine, Benjamin D.; Pawelczyk, James A.; Zuckerman, Julie; Zhang, Rong; Fu, Qi; Iwasaki, Kenichi; Ray, Chet; Blomqvist, C. Gunnar; Lane, Lynda D.; Giller, Cole A.

    2003-01-01

    During the acute transition from lying supine to standing upright, a large volume of blood suddenly moves from the chest into the legs. To prevent fainting, the blood pressure control system senses this change immediately, and rapidly adjusts flow (by increasing heart rate) and resistance to flow (by constricting the blood vessels) to restore blood pressure and maintain brain blood flow. If this system is inadequate, the brain has a backup plan. Blood vessels in the brain can adjust their diameter to keep blood flow constant. If blood pressure drops, the brain blood vessels dilate; if blood pressure increases, the brain blood vessels constrict. This process, which is called autoregulation, allows the brain to maintain a steady stream of oxygen, even when blood pressure changes. We examined what changes in the blood pressure control system or cerebral autoregulation contribute to the blood pressure control problems seen after spaceflight. We asked: (1) does the adaptation to spaceflight cause an adaptation in the blood pressure control system that impairs the ability of the system to constrict blood vessels on return to Earth?; (2) if such a defect exists, could we pinpoint the neural pathways involved?; and (3) does cerebral autoregulation become abnormal during spaceflight, impairing the body s ability to maintain constant brain blood flow when standing upright on Earth? We stressed the blood pressure control system using lower body negative pressure, upright tilt, handgrip exercise, and cold stimulation of the hand. Standard cardiovascular parameters were measured along with sympathetic nerve activity (the nerve activity causing blood vessels to constrict) and brain blood flow. We confirmed that the primary cardiovascular effect of spaceflight was a postflight reduction in upright stroke volume (the amount of blood the heart pumps per beat). Heart rate increased appropriately for the reduction in stroke volume, thereby showing that changes in heart rate

  3. Neural networks for control of NO{sub x} emissions in fossil plants

    SciTech Connect

    Reifman, J.; Feldman, E.E.

    1997-04-01

    We discuss the use of two classes of artificial neural networks, multilayer feedforward networks and fully-recurrent networks, in the development of a closed-loop controller for discrete-time dynamical systems. We apply the neural system to the control of oxides of nitrogen (NO{sub x}) emissions for a simplified representation of a furnace of a coal-fired fossil plant. Plant data from one of Commonwealth Edison`s fossil power plants were used to build a recurrent neural model of NO{sub x} formation which is then used in the training of the feedforward neural controller. Preliminary simulation results demonstrate the feasibility of the approach and additional tests with increasingly realistic models should be pursued.

  4. Neural Network for Image-to-Image Control of Optical Tweezers

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Anderson, Robert C.; Weiland, Kenneth E.; Wrbanek, Susan Y.

    2004-01-01

    A method is discussed for using neural networks to control optical tweezers. Neural-net outputs are combined with scaling and tiling to generate 480 by 480-pixel control patterns for a spatial light modulator (SLM). The SLM can be combined in various ways with a microscope to create movable tweezers traps with controllable profiles. The neural nets are intended to respond to scattered light from carbon and silicon carbide nanotube sensors. The nanotube sensors are to be held by the traps for manipulation and calibration. Scaling and tiling allow the 100 by 100-pixel maximum resolution of the neural-net software to be applied in stages to exploit the full 480 by 480-pixel resolution of the SLM. One of these stages is intended to create sensitive null detectors for detecting variations in the scattered light from the nanotube sensors.

  5. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    PubMed

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model. PMID:19964991

  6. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    NASA Technical Reports Server (NTRS)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  7. A Neural Auto-depth Controller for an Unmanned Underwater Vehicle

    NASA Astrophysics Data System (ADS)

    Sutton, R.; Johnson, C.; Roberts, G. N.

    Artificial neural networks offer an alternative strategy for the nonlinear control of unmanned underwater vehicles (UUVS). This paper investigates the use of a multi-layered perceptron (MLP) network in controlling an UUV over a sea-bed profile and compares the use of applying chemotaxis learning to that of the more commonly employed back propagation algorithm. The results show that, for differing sized MLPs, the chemotaxis algorithm produces a successful controller over the sea-bed profile in an improved training time. Also it will be shown that, in the presence of noise and change in vehicle mass, the neural controller out-performed a classical proportional-integral-derivative controller.

  8. Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation in vivo

    PubMed Central

    Olivera-Martinez, Isabel; Schurch, Nick; Li, Roman A.; Song, Junfang; Halley, Pamela A.; Das, Raman M.; Burt, Dave W.; Barton, Geoffrey J.; Storey, Kate G.

    2014-01-01

    Here, we exploit the spatial separation of temporal events of neural differentiation in the elongating chick body axis to provide the first analysis of transcriptome change in progressively more differentiated neural cell populations in vivo. Microarray data, validated against direct RNA sequencing, identified: (1) a gene cohort characteristic of the multi-potent stem zone epiblast, which contains neuro-mesodermal progenitors that progressively generate the spinal cord; (2) a major transcriptome re-organisation as cells then adopt a neural fate; and (3) increasing diversity as neural patterning and neuron production begin. Focussing on the transition from multi-potent to neural state cells, we capture changes in major signalling pathways, uncover novel Wnt and Notch signalling dynamics, and implicate new pathways (mevalonate pathway/steroid biogenesis and TGFβ). This analysis further predicts changes in cellular processes, cell cycle, RNA-processing and protein turnover as cells acquire neural fate. We show that these changes are conserved across species and provide biological evidence for reduced proteasome efficiency and a novel lengthening of S phase. This latter step may provide time for epigenetic events to mediate large-scale transcriptome re-organisation; consistent with this, we uncover simultaneous downregulation of major chromatin modifiers as the neural programme is established. We further demonstrate that transcription of one such gene, HDAC1, is dependent on FGF signalling, making a novel link between signals that control neural differentiation and transcription of a core regulator of chromatin organisation. Our work implicates new signalling pathways and dynamics, cellular processes and epigenetic modifiers in neural differentiation in vivo, identifying multiple new potential cellular and molecular mechanisms that direct differentiation. PMID:25063452

  9. Neural Issues in the Control of Muscular Strength

    ERIC Educational Resources Information Center

    Kamen, Gary

    2004-01-01

    During the earliest stages of resistance exercise training, initial muscular strength gains occur too rapidly to be explained solely by muscle-based mechanisms. However, increases in surface-based EMG amplitude as well as motor unit discharge rate provide some insight to the existence of neural mechanisms in the earliest phases of resistance…

  10. Neural control of inflammation by the greater splanchnic nerves

    PubMed Central

    Martelli, Davide; Yao, Song T; McKinley, Michael J; McAllen, Robin M

    2014-01-01

    The brain influences immune function through a powerful neural reflex that suppresses the release of a key pro-inflammatory cytokine, tumor necrosis factor α, after immune challenge. The efferent motor pathway of this reflex is in the splanchnic nerves, not the vagi. This reflex regulates inflammation but does not suppress fever.

  11. Improved training of neural networks for the nonlinear active control of sound and vibration.

    PubMed

    Bouchard, M; Paillard, B; Le Dinh, C T

    1999-01-01

    Active control of sound and vibration has been the subject of a lot of research in recent years, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the structure to be controlled exhibits a nonlinear behavior. A multilayer perceptron neural-network based control structure was previously introduced as a nonlinear active controller, with a training algorithm based on an extended backpropagation scheme. This paper introduces new heuristical training algorithms for the same neural-network control structure. The objective is to develop new algorithms with faster convergence speed (by using nonlinear recursive-least-squares algorithms) and/or lower computational loads (by using an alternative approach to compute the instantaneous gradient of the cost function). Experimental results of active sound control using a nonlinear actuator with linear and nonlinear controllers are presented. The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural-network controller can outperform linear controllers. PMID:18252535

  12. Negative chemotaxis does not control quail neural crest cell dispersion.

    PubMed

    Erickson, C A; Olivier, K R

    1983-04-01

    Negative chemotaxis has been proposed to direct dispersion of amphibian neural crest cells away from the neural tube (V. C. Twitty, 1949, Growth 13(Suppl. 9), 133-161). We have reexamined this hypothesis using quail neural crest and do not find evidence for it. When pigmented or freshly isolated neural crest cells are covered by glass shards to prevent diffusion of a "putative" chemotactic agent away from the cells and into the medium, we find a decrease in density of cells beneath the coverslip as did Twitty and Niu (1948, J. Exp. Zool. 108, 405-437). Unlike those investigators, however, we find the covered cells move slower than uncovered cells and that the decrease in density can be attributed to cessation of cell division and increased cell death in older cultures, rather than directed migration away from each other. In cell systems where negative chemotaxis has been demonstrated, a "no man's land" forms between two confronted explants (Oldfield, 1963, Exp. Cell Res. 30, 125-138). No such cell-free space forms between confronted neural crest explants, even if the explants are closely covered to prevent diffusion of the negative chemotactic material. If crest cell aggregates are drawn into capillary tubes to allow accumulation of the putative material, the cells disperse farther, the wider the capillary tube bore. This is contrary to what would be expected if dispersion depended on accumulation of this material. Also, no difference in dispersion is noted between cells in the center of the tubes versus cells near the mouth of the tubes where the tube medium is freely exchanging with external fresh medium. Alternative hypotheses for directionality of crest migration in vivo are discussed. PMID:6832483

  13. Imaging the neural circuitry and chemical control of aggressive motivation

    PubMed Central

    Ferris, Craig F; Stolberg, Tara; Kulkarni, Praveen; Murugavel, Murali; Blanchard, Robert; Blanchard, D Caroline; Febo, Marcelo; Brevard, Mathew; Simon, Neal G

    2008-01-01

    Background With the advent of functional magnetic resonance imaging (fMRI) in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior. Results To trigger aggressive motivation, male rats were presented with their female cage mate plus a novel male intruder in the bore of the magnet during image acquisition. As expected, brain areas previously identified as critical in the organization and expression of aggressive behavior were activated, e.g., lateral hypothalamus, medial basal amygdala. Unexpected was the intense activation of the forebrain cortex and anterior thalamic nuclei. Oral administration of a selective vasopressin V1a receptor antagonist SRX251 or the selective serotonin reuptake inhibitor fluoxetine, drugs that block aggressive behavior, both caused a general suppression of the distributed neural circuit involved in aggressive motivation. However, the effect of SRX251, but not fluoxetine, was specific to aggression as brain activation in response to a novel sexually receptive female was unaffected. Conclusion The putative neural circuit of aggressive motivation identified with fMRI includes neural substrates contributing to emotional expression (i.e. cortical and medial amygdala, BNST, lateral hypothalamus), emotional experience (i.e. hippocampus, forebrain cortex, anterior cingulate, retrosplenial cortex) and the anterior thalamic nuclei that bridge the motor and cognitive components of aggressive responding. Drugs that block vasopressin

  14. Multipotent neurogenic fate of mesenchymal stem cell is determined by Cdk4-mediated hypophosphorylation of Smad-STAT3.

    PubMed

    Kim, Dong-Young; Lee, Janet; Kang, Dongrim; Lee, Do-Hyeong; Kim, Yoon-Ja; Hwang, Sang-Gu; Kim, Dong-Ik; Lee, Chang-Woo; Lee, Kyung-Hoon

    2016-07-01

    Cyclin-dependent kinase (Cdk) in complex with a corresponding cyclin plays a pivotal role in neurogenic differentiation. In particular, Cdk4 activity acts as a signaling switch to direct human mesenchymal stem cells (MSCs) to neural transdifferentiation. However, the molecular evidence of how Cdk4 activity converts MSCs to neurogenic lineage remains unknown. Here, we found that Cdk4 inhibition in human MSCs enriches the populations of neural stem and progenitor pools rather than differentiated glial and neuronal cell pools. Interestingly, Cdk4 inhibition directly inactivates Smads and subsequently STAT3 signaling by hypophosphorylation, and both Cdk4 and Smads levels are linked during the processes of neural transdifferentiation and differentiation. In summary, our results provide novel molecular evidence in which Cdk4 inhibition leads to directing human MSCs to a multipotent neurogenic fate by inactivating Smads-STAT3 signaling. PMID:27192561

  15. Evaluating the performances of statistical and neural network based control charts

    NASA Astrophysics Data System (ADS)

    Teoh, Kok Ban; Ong, Hong Choon

    2015-10-01

    Control chart is used widely in many fields and traditional control chart is no longer adequate in detecting a sudden change in a particular process. So, run rules which are built in into Shewhart X ¯ control chart while Exponential Weighted Moving Average control chart (EWMA), Cumulative Sum control chart (CUSUM) and neural network based control chart are introduced to overcome the limitation regarding to the sensitivity of traditional control chart. In this study, the average run length (ARL) and median run length (MRL) in the shifts in the process mean of control charts mentioned will be computed. We will show that interpretations based only on the ARL can be misleading. Thus, MRL is also used to evaluate the performances of the control charts. From this study, neural network based control chart is found to possess a better performance than run rules of Shewhart X ¯ control chart, EWMA and CUSUM control chart.

  16. Muscle Extracellular Matrix Scaffold Is a Multipotent Environment

    PubMed Central

    Aulino, Paola; Costa, Alessandra; Chiaravalloti, Ernesto; Perniconi, Barbara; Adamo, Sergio; Coletti, Dario; Marrelli, Massimo; Tatullo, Marco; Teodori, Laura

    2015-01-01

    The multipotency of scaffolds is a new concept. Skeletal muscle acellular scaffolds (MAS) implanted at the interface of Tibialis Anterior/tibial bone and masseter muscle/mandible bone in a murine model were colonized by muscle cells near the host muscle and by bone-cartilaginous tissues near the host bone, thus highlighting the importance of the environment in directing cell homing and differentiation. These results unveil the multipotency of MAS and point to the potential of this new technique as a valuable tool in musculo-skeletal tissue regeneration. PMID:25897295

  17. A neural manufacturing a novel concept for processing modeling, monitoring and control

    SciTech Connect

    Law, B.; Fu, C.Y.; Petrich, L.

    1995-10-01

    Semiconductor fabrication lines have become extremely costly, and achieving a good return from such a high capital investment requires efficient utilization of these expensive facilities. It is highly desirable to shorten processing development time, increase fabrication yield, enhance flexibility, improve quality, and minimize downtime. We propose that these ends can be achieved by applying recent advances in the areas of artificial neural networks, fuzzy logic, machine learning, and genetic algorithms. We use the term neural manufacturing to describe such applications. This paper describes our use of artificial neural networks to improve the monitoring and control of semiconductor process.

  18. Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

    PubMed

    Yang, Shiju; Li, Chuandong; Huang, Tingwen

    2016-03-01

    The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. PMID:26797471

  19. Parameter estimation and control for a neural mass model based on the unscented Kalman filter

    NASA Astrophysics Data System (ADS)

    Liu, Xian; Gao, Qing

    2013-10-01

    Recent progress in Kalman filters to estimate states and parameters in nonlinear systems has provided the possibility of applying such approaches to neural systems. We here apply the nonlinear method of unscented Kalman filters (UKFs) to observe states and estimate parameters in a neural mass model that can simulate distinct rhythms in electroencephalography (EEG) including dynamical evolution during epilepsy seizures. We demonstrate the efficiency of the UKF in estimating states and parameters. We also develop an UKF-based control strategy to modulate the dynamics of the neural mass model. In this strategy the UKF plays the role of observing states, and the control law is constructed via the estimated states. We demonstrate the feasibility of using such a strategy to suppress epileptiform spikes in the neural mass model.

  20. An input-output based robust stabilization criterion for neural-network control of nonlinear systems.

    PubMed

    Fernández de Cañete, J; Barreiro, A; García-Cerezo, A; García-Moral, I

    2001-01-01

    A stabilization method based on the input-output conicity criterion is presented. Conventional learning algorithms are applied to adjust the controller dynamics, and robust stability of the closed-loop system is guaranteed by modifying the training patterns which yield unstable behavior. The methodology developed expands the class of nonlinear systems to be controlled using neural control schemes, so that the stabilization of a broad class of neural-network-based control systems, even with unknown dynamics, is assured. Straightforwardness in the application of this method is evident in contrast to the Lyapunov function approach. PMID:18249978

  1. Dynamic control of ROV`s making use of the neural network concept

    SciTech Connect

    Ooi, Tadashi; Yoshida, Yuki; Takahashi, Yoshiaki; Kidoushi, Hideki

    1994-12-31

    An attempt is made to combine the classical controller with the concept of neural network, the result of which is a control system that they have named the Robust Adaptive Neural-net Controller (RANC). The RANC identifies the dynamic characteristics of the remotely operated vehicle (ROV) including its ambient environment involving cyclic disturbances such as forces induced by waves, and organizes automatically an optimized controller. A tank experiment is described in which the RANC is set to maintain a model ROV at a prescribed depth of water under artificially generated wave disturbance.

  2. Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

    PubMed Central

    Pedrocchi, Alessandra; Ferrante, Simona; De Momi, Elena; Ferrigno, Giancarlo

    2006-01-01

    Background The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required. Methods The error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out. Results The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances. Conclusion Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice. PMID:17029636

  3. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    PubMed

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design. PMID:26259222

  4. Global exponential stability of recurrent neural networks for synthesizing linear feedback control systems via pole assignment.

    PubMed

    Zhang, Yunong; Wang, Jun

    2002-01-01

    Global exponential stability is the most desirable stability property of recurrent neural networks. The paper presents new results for recurrent neural networks applied to online computation of feedback gains of linear time-invariant multivariable systems via pole assignment. The theoretical analysis focuses on the global exponential stability, convergence rates, and selection of design parameters. The theoretical results are further substantiated by simulation results conducted for synthesizing linear feedback control systems with different specifications and design requirements. PMID:18244461

  5. Real-time water treatment process control with artificial neural networks

    SciTech Connect

    Zhang, Q.; Stanley, S.J.

    1999-02-01

    With more stringent requirements being placed on water treatment performance, operators need a reliable tool to optimize the process control in the treatment plant. In the present paper, one such tool is presented, which is a process control system built with the artificial neural network (ANN) modeling approach. The coagulation, flocculation, and sedimentation processes involve many complex physical and chemical phenomena and thus are difficult to model for process control with traditional methods. Proposed is the use of a neural network process control system for the coagulation, flocculation, and sedimentation processes. Presented is a review of influential control parameters and control requirements for these processes followed by the development of a feed forward neural network control scheme. A neural network process model was built based on nearly 2,000 sets of process control data. This model formed the major component of a software controller and was found to consistently predict the optimum alum and power activated carbon doses for different control actions. With minor modifications, the approach illustrated can be used for building control models for other water treatment processes.

  6. Effects of hypoxia on sympathetic neural control in humans

    NASA Technical Reports Server (NTRS)

    Smith, M. L.; Muenter, N. K.

    2000-01-01

    This special issue is principally focused on the time domain of the adaptive mechanisms of ventilatory responses to short-term, long-term and intermittent hypoxia. The purpose of this review is to summarize the limited literature on the sympathetic neural responses to sustained or intermittent hypoxia in humans and attempt to discern the time domain of these responses and potential adaptive processes that are evoked during short and long-term exposures to hypoxia.

  7. Controlling chaos in balanced neural circuits with input spike trains

    NASA Astrophysics Data System (ADS)

    Engelken, Rainer; Wolf, Fred

    The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.

  8. Predictive and Neural Predictive Control of Uncertain Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.

    2000-01-01

    Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

  9. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

    PubMed Central

    Benyamini, Miri; Zacksenhouse, Miriam

    2015-01-01

    Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal. PMID:26042002

  10. Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2007-01-01

    This paper describes the performance of a simplified dynamic inversion controller with neural network supplementation. This 6 DOF (Degree-of-Freedom) simulation study focuses on the results with and without adaptation of neural networks using a simulation of the NASA modified F-15 which has canards. One area of interest is the performance of a simulated surface failure while attempting to minimize the inertial cross coupling effect of a [B] matrix failure (a control derivative anomaly associated with a jammed or missing control surface). Another area of interest and presented is simulated aerodynamic failures ([A] matrix) such as a canard failure. The controller uses explicit models to produce desired angular rate commands. The dynamic inversion calculates the necessary surface commands to achieve the desired rates. The simplified dynamic inversion uses approximate short period and roll axis dynamics. Initial results indicated that the transient response for a [B] matrix failure using a Neural Network (NN) improved the control behavior when compared to not using a neural network for a given failure, However, further evaluation of the controller was comparable, with objections io the cross coupling effects (after changes were made to the controller). This paper describes the methods employed to reduce the cross coupling effect and maintain adequate tracking errors. The IA] matrix failure results show that control of the aircraft without adaptation is more difficult [leas damped) than with active neural networks, Simulation results show Neural Network augmentation of the controller improves performance in terms of backing error and cross coupling reduction and improved performance with aerodynamic-type failures.

  11. Neural network controller development for a magnetically suspended flywheel energy storage system

    NASA Technical Reports Server (NTRS)

    Fittro, Roger L.; Pang, Da-Chen; Anand, Davinder K.

    1994-01-01

    A neural network controller has been developed to accommodate disturbances and nonlinearities and improve the robustness of a magnetically suspended flywheel energy storage system. The controller is trained using the back propagation-through-time technique incorporated with a time-averaging scheme. The resulting nonlinear neural network controller improves system performance by adapting flywheel stiffness and damping based on operating speed. In addition, a hybrid multi-layered neural network controller is developed off-line which is capable of improving system performance even further. All of the research presented in this paper was implemented via a magnetic bearing computer simulation. However, careful attention was paid to developing a practical methodology which will make future application to the actual bearing system fairly straightforward.

  12. Towards an Irritable Bowel Syndrome Control System Based on Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Podolski, Ina; Rettberg, Achim

    To solve health problems with medical applications that use complex algorithms is a trend nowadays. It could also be a chance to help patients with critical problems caused from nerve irritations to overcome them and provide a better living situation. In this paper a system for monitoring and controlling the nerves from the intestine is described on a theoretical basis. The presented system could be applied to the irritable bowel syndrome. For control a neural network is used. The advantages for using a neural network for the control of irritable bowel syndrome are the adaptation and learning. These two aspects are important because the syndrome behavior varies from patient to patient and have also concerning the time a lot of variations with respect to each patient. The developed neural network is implemented and can be simulated. Therefore, it can be shown how the network monitor and control the nerves for individual input parameters.

  13. Quasi-Newton training in supervised neural networks: An application to process control

    SciTech Connect

    Ferrigno, S.; Achenie, L.

    1996-12-31

    In recent years, neural networks have found uses in chemical process control, modeling and diagnosis. In spite of the promise that these neural network paradigms hold, there are several challenges we often face when using these neural networks. These include but are not limited to (1) how fast training can be achieved and (2) how to pick the network topology. In the oral presentation, I will discuss the use of Newton-type strategies and parallel computing to address the first issue. In this paper, however, a quasi-Newton training strategy is used to carry out control of the liquid level in a serially linked draining tank problem. It has been demonstrated that a neuro-controller (trained off-line) performs at least as well as the PID family of controllers on laboratory scale sequential draining tanks. 5 refs., 8 figs.

  14. Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems.

    PubMed

    Xu, Hao; Jagannathan, Sarangapani

    2015-03-01

    The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme. PMID:25720004

  15. Neural network-based control for the fiber placement composite manufacturing process

    NASA Astrophysics Data System (ADS)

    Lichtenwalner, P. F.

    1993-10-01

    At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine.

  16. Transient Stability Enhancement of Power Systems by Lyapunov-Based Recurrent Neural Networks UPFC Controllers

    NASA Astrophysics Data System (ADS)

    Chu, Chia-Chi; Tsai, Hung-Chi; Chang, Wei-Neng

    A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.

  17. Neural solution to the correction of miss distance in gun fire control system.

    PubMed

    Kim, J G; Jeong, H; Lee, Y W

    1997-01-01

    Multilayer perceptrons trained with the backpropagation algorithm are tested in gun fire control system for error correction and are compared to optimal algorithms based on minimum mean square error. The structure of the proposed neural controller is described and performance results are shown. PMID:10065830

  18. Neural network based supervisory & closed loop controls for NOx emission reductions and heat rate improvement

    SciTech Connect

    Radl, B.J.; Corfman, D.; Kish, B.

    1995-12-31

    This paper discusses the operational experience gained from installing a neural network based supervisor setpoint control system for selected combustion parameters at Penn Power`s New Castle station. The primary goal of the program is to reduce NOx emissions while maintaining or improving heat rate. The program was jointly funded by Ohio Edison, U.S. Department of Energy (DOE) and Pegasus Technologies Corp. The target power station, Penn Power`s New Castle Unit 5, is a 1950`s vintage Babcock & Wilcox wall fired furnace with gross generation capacity of 150 MW. Before installation of the neural network system (NeuSIGHT), NOx averaged 0.75 to 0.80 lbs/mbtu at full load conditions. Previous testing reduced this from 1.0 lbs/mbtu under normal operating conditions. To meet the new Pennsylvania DER limits, which set an absolute tonnage limit on NOx, and operate for a full year, a further NOx reduction of 20% was required. The control system setup interfaced a Unix workstation to a Bailey Controls N90 DCS. The neural network and data collection/processing system resided on the workstation. New setpoints were determined by the neural network periodically. These setpoints were constrained within existing control system limits. The objective was to model the multi-dimensional and non-linear problem of NOx formation in the furnace with a neural network. Once modeled the neural network performed many {open_quote}what if{close_quote} simulations to optimize setpoints for the current operating conditions. To keep up with changes in operating conditions the neural network was set to continually learn from the most recent set of measurements. Conditioning algorithms for the input data and output setpoints were developed to handle the inherently {open_quote}noisy{close_quote} input data and to provide stable output recommendations. Test results and parameters used for combustion optimization are summarized in this paper.

  19. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    PubMed

    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W; Sanchez, Justin C

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

  20. Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

    PubMed Central

    Pohlmeyer, Eric A.; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W.; Sanchez, Justin C.

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

  1. Translating Principles of Neural Plasticity into Research on Speech Motor Control Recovery and Rehabilitation

    PubMed Central

    Ludlow, Christy L.; Hoit, Jeannette; Kent, Raymond; Ramig, Lorraine O.; Shrivastav, Rahul; Strand, Edythe; Yorkston, Kathryn; Sapienza, Christine

    2008-01-01

    Purpose To review the principles of neural plasticity and make recommendations for research on the neural bases for rehabilitation of neurogenic speech disorders. Method A working group in speech motor control and disorders developed this report, which examines the potential relevance of basic research on the brain mechanisms involved in neural plasticity and discusses possible similarities and differences for application to speech motor control disorders. The possible involvement of neural plasticity in changes in speech production in normalcy, development, aging, and neurological diseases and disorders was considered. This report focuses on the appropriate use of functional and structural neuroimaging and the design of feasibility studies aimed at understanding how brain mechanisms are altered by environmental manipulations such as training and stimulation and how these changes might enhance the future development of rehabilitative methods for persons with speech motor control disorders. Conclusions Increased collaboration with neuroscientists working in clinical research centers addressing human communication disorders might foster research in this area. It is hoped that this paper will encourage future research on speech motor control disorders to address the principles of neural plasticity and their application for rehabilitation. PMID:18230849

  2. Neuromechanic: a computational platform for simulation and analysis of the neural control of movement

    PubMed Central

    Bunderson, Nathan E.; Bingham, Jeffrey T.; Sohn, M. Hongchul; Ting, Lena H.; Burkholder, Thomas J.

    2015-01-01

    Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states as well as muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization and stability analysis tools to provide structural insights into the neural control of movement. PMID:23027632

  3. Comparison of nestin-expressing multipotent stem cells in the tongue fungiform papilla and vibrissa hair follicle.

    PubMed

    Mii, Sumiyuki; Amoh, Yasuyuki; Katsuoka, Kensei; Hoffman, Robert M

    2014-06-01

    We have previously reported that hair follicles contain multipotent stem cells, which express nestin and participate in follicle growth at anagen as well as in the extension of the follicle sensory nerve. The nestin-driven green fluorescent protein (ND-GFP) transgenic mouse labels all nestin-expressing cells with GFP. The hair follicle nestin-GFP cells can differentiate into neurons, Schwann cells, and other cell types. In this study, we describe nestin-expressing multipotent stem cells in the fungiform papilla in the tongue. The nestin-expressing multipotent stem cells in the fungiform papilla are located around a peripheral sensory nerve immediately below the taste bud and co-express the neural crest cell marker p75(NTR) . The fungiform papilla cells formed spheres in suspension culture in DMEM-F12 medium supplemented with basic fibroblast growth factor (bFGF). The spheres consisted of nestin-expressing cells that co-expressed the neural crest marker p75(NTR) and which developed expression of the stem cell marker CD34. P75(NTR), CD34 and nestin co-expression suggested that nestin-expressing cells comprising the fungiform papilla spheres were in a relatively undifferentiated state. The nestin-expressing cells of these spheres acquired the following markers: β III tubulin typical of nerve cells; GFAP typical of glial cells; K15 typical of keratinocytes; and smooth-muscle antigen (SMA), after transfer to RPMI 1640 medium with 10% fetal bovine serum (FBS), suggesting they differentiated into multiple cell types. The results of the current study indicate nestin-expressing fungiform papilla cells and the nestin-expressing hair follicle stem cells have common features of cell morphology and ability to differentiate into multiple cell types, suggesting their remarkable similarity. PMID:24142339

  4. Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network

    NASA Astrophysics Data System (ADS)

    Ekkachai, Kittipong; Tungpimolrut, Kanokvate; Nilkhamhang, Itthisek

    2013-11-01

    An inverse controller is proposed for a magnetorheological (MR) damper that consists of a hysteresis model and a voltage controller. The force characteristics of the MR damper caused by excitation signals are represented by a feedforward neural network (FNN) with an elementary hysteresis model (EHM). The voltage controller is constructed using another FNN to calculate a suitable input signal that will allow the MR damper to produce the desired damping force. The performance of the proposed EHM-based FNN controller is experimentally compared to existing control methodologies, such as clipped-optimal control, signum function control, conventional FNN, and recurrent neural network with displacement or velocity inputs. The results show that the proposed controller, which does not require force feedback to implement, provides excellent accuracy, fast response time, and lower energy consumption.

  5. Adaptive neural network nonlinear control for BTT missile based on the differential geometry method

    NASA Astrophysics Data System (ADS)

    Wu, Hao; Wang, Yongji; Xu, Jiangsheng

    2007-11-01

    A new nonlinear control strategy incorporated the differential geometry method with adaptive neural networks is presented for the nonlinear coupling system of Bank-to-Turn missile in reentry phase. The basic control law is designed using the differential geometry feedback linearization method, and the online learning neural networks are used to compensate the system errors due to aerodynamic parameter errors and external disturbance in view of the arbitrary nonlinear mapping and rapid online learning ability for multi-layer neural networks. The online weights and thresholds tuning rules are deduced according to the tracking error performance functions by Levenberg-Marquardt algorithm, which will make the learning process faster and more stable. The six degree of freedom simulation results show that the attitude angles can track the desired trajectory precisely. It means that the proposed strategy effectively enhance the stability, the tracking performance and the robustness of the control system.

  6. Some neural correlates of sensorial and cognitive control of behavior

    NASA Astrophysics Data System (ADS)

    Ogmen, Haluk; Prakash, R. V.; Moussa, M.

    1992-07-01

    Development and maintenance of unsupervised intelligent activity relies on an active interaction with the environment. Such active exploratory behavior plays an essential role in both the development and adult phases of higher biological systems including humans. Exploration initiates a self-organization process whereby a coherent fusion of different sensory and motor modalities can be achieved (sensory-motor development) and maintained (adult rearrangement). In addition, the development of intelligence depends critically on an active manipulation of the environment. These observations are in sharp contrast with current attempts of artificial intelligence and various neural network models. In this paper, we present a neural network model that combines internal drives and environmental cues to reach behavioral decisions for the exploratory activity. The vision system consists of an ambient and a focal system. The ambient vision system guides eye movements by using nonassociative learning. This sensory based attentional focusing is augmented by a `cognitive' system using models developed for various aspects of frontal lobe function. The combined system has nonassociative learning, reinforcement learning, selective attention, habit formation, and flexible criterion categorization properties.

  7. Axonal Control of the Adult Neural Stem Cell Niche

    PubMed Central

    Tong, Cheuk Ka; Chen, Jiadong; Cebrián-Silla, Arantxa; Mirzadeh, Zaman; Obernier, Kirsten; Guinto, Cristina D.; Tecott, Laurence H.; García-Verdugo, Jose Manuel; Kriegstein, Arnold; Alvarez-Buylla, Arturo

    2014-01-01

    SUMMARY The ventricular-subventricular zone (V-SVZ) is an extensive germinal niche containing neural stem cells (NSC) in the walls of the lateral ventricles of the adult brain. How the adult brain’s neural activity influences the behavior of adult NSCs remains largely unknown. We show that serotonergic (5HT) axons originating from a small group of neurons in the raphe form an extensive plexus on most of the ventricular walls. Electron microscopy revealed intimate contacts between 5HT axons and NSCs (B1) or ependymal cells (E1) and these cells were labeled by a transsynaptic viral tracer injected into the raphe. B1 cells express the 5HT receptors 2C and 5A. Electrophysiology showed that activation of these receptors in B1 cells induced small inward currents. Intraventricular infusion of 5HT2C agonist or antagonist increased or decreased V-SVZ proliferation, respectively. These results indicate that supraependymal 5HT axons directly interact with NSCs to regulate neurogenesis via 5HT2C. PMID:24561083

  8. Axonal control of the adult neural stem cell niche.

    PubMed

    Tong, Cheuk Ka; Chen, Jiadong; Cebrián-Silla, Arantxa; Mirzadeh, Zaman; Obernier, Kirsten; Guinto, Cristina D; Tecott, Laurence H; García-Verdugo, Jose Manuel; Kriegstein, Arnold; Alvarez-Buylla, Arturo

    2014-04-01

    The ventricular-subventricular zone (V-SVZ) is an extensive germinal niche containing neural stem cells (NSCs) in the walls of the lateral ventricles of the adult brain. How the adult brain's neural activity influences the behavior of adult NSCs remains largely unknown. We show that serotonergic (5HT) axons originating from a small group of neurons in the raphe form an extensive plexus on most of the ventricular walls. Electron microscopy revealed intimate contacts between 5HT axons and NSCs (B1) or ependymal cells (E1) and these cells were labeled by a transsynaptic viral tracer injected into the raphe. B1 cells express the 5HT receptors 2C and 5A. Electrophysiology showed that activation of these receptors in B1 cells induced small inward currents. Intraventricular infusion of 5HT2C agonist or antagonist increased or decreased V-SVZ proliferation, respectively. These results indicate that supraependymal 5HT axons directly interact with NSCs to regulate neurogenesis via 5HT2C. PMID:24561083

  9. A Pressure Control Method for Emulsion Pump Station Based on Elman Neural Network

    PubMed Central

    Tan, Chao; Qi, Nan; Yao, Xingang; Wang, Zhongbin; Si, Lei

    2015-01-01

    In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others. PMID:25861253

  10. A pressure control method for emulsion pump station based on Elman neural network.

    PubMed

    Tan, Chao; Qi, Nan; Zhou, Xin; Liu, Xinhua; Yao, Xingang; Wang, Zhongbin; Si, Lei

    2015-01-01

    In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others. PMID:25861253

  11. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control

    PubMed Central

    Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit

    2015-01-01

    In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain–machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations. PMID:26321943

  12. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.

    PubMed

    Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit

    2015-01-01

    In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations. PMID:26321943

  13. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    NASA Technical Reports Server (NTRS)

    Williams, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed a pitch frequency sweep and an automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.

  14. Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

    PubMed

    Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

    2014-01-01

    An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results. PMID:25147871

  15. On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Rubaai, Ahmed

    1996-01-01

    A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

  16. Bilingualism increases neural response consistency and attentional control: evidence for sensory and cognitive coupling.

    PubMed

    Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Auditory processing is presumed to be influenced by cognitive processes - including attentional control - in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] in adolescent Spanish-English bilinguals and English monolinguals and separately obtained measures of attentional control and language ability. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. PMID:24413593

  17. Bilingualism increases neural response consistency and attentional control: Evidence for sensory and cognitive coupling

    PubMed Central

    Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Auditory processing is presumed to be influenced by cognitive processes – including attentional control – in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] and separately obtained measures of attentional control and language ability in adolescent Spanish-English bilinguals and English monolinguals. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. PMID:24413593

  18. Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control.

    PubMed

    Zhang, Wei; Li, Chuandong; Huang, Tingwen; Xiao, Mingqing

    2015-11-01

    In this paper, the synchronization problem for neural networks with stochastic perturbation is studied with intermittent control via adaptive aperiodicity. Under the framework of stochastic theory and Lyapunov stability method, we develop some techniques of intermittent control with adaptive aperiodicity to achieve the synchronization of a class of neural networks, modeled by stochastic systems. Some effective sufficient conditions are established for the realization of synchronization of the underlying network. Numerical simulations of two examples are provided to illustrate the theoretical results obtained in the paper. PMID:26319051

  19. Functional Multipotency of Stem Cells: A Conceptual Review of Neurotrophic Factor-Based Evidence and Its Role in Translational Research

    PubMed Central

    Teng, Yang D; Yu, Dou; Ropper, Alexander E; Li, Jianxue; Kabatas, Serdar; Wakeman, Dustin R; Wang, Junmei; Sullivan, Maryrose P; Redmond, D. Eugene; Langer, Robert; Snyder, Evan Y; Sidman, Richard L

    2011-01-01

    We here propose an updated concept of stem cells (SCs), with an emphasis on neural stem cells (NSCs). The conventional view, which has touched principally on the essential property of lineage multipotency (e.g., the ability of NSCs to differentiate into all neural cells), should be broadened to include the emerging recognition of biofunctional multipotency of SCs to mediate systemic homeostasis, evidenced in NSCs in particular by the secretion of neurotrophic factors. Under this new conceptual context and taking the NSC as a leading example, one may begin to appreciate and seek the “logic” behind the wide range of molecular tactics the NSC appears to serve at successive developmental stages as it integrates into and prepares, modifies, and guides the surrounding CNS micro- and macro-environment towards the formation and self-maintenance of a functioning adult nervous system. We suggest that embracing this view of the “multipotency” of the SCs is pivotal for correctly, efficiently, and optimally exploiting stem cell biology for therapeutic applications, including reconstitution of a dysfunctional CNS. PMID:22654717

  20. Dynamic neural networking as a basis for plasticity in the control of heart rate.

    PubMed

    Kember, G; Armour, J A; Zamir, M

    2013-01-21

    A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. PMID:23041448

  1. Using Neural Networks to Explore Air Traffic Controller Workload

    NASA Technical Reports Server (NTRS)

    Martin, Lynne; Kozon, Thomas; Verma, Savita; Lozito, Sandra C.

    2006-01-01

    When a new system, concept, or tool is proposed in the aviation domain, one concern is the impact that this will have on operator workload. As an experience, workload is difficult to measure in a way that will allow comparison of proposed systems with those already in existence. Chatterji and Sridhar (2001) suggested a method by which airspace parameters can be translated into workload ratings, using a neural network. This approach was employed, and modified to accept input from a non-real time airspace simulation model. The following sections describe the preparations and testing work that will enable comparison of a future airspace concept with a current day baseline in terms of workload levels.

  2. Neural Control of Non-vasomotor Organs in Hypertension.

    PubMed

    Hurr, Chansol; Young, Colin N

    2016-04-01

    Hypertension affects over 25 % of the population with the incidence continuing to rise, due in part to the growing obesity epidemic. Chronic elevations in sympathetic nerve activity (SNA) are a hallmark of the disease and contribute to elevations in blood pressure through influences on the vasculature, kidney, and heart (i.e., neurogenic hypertension). In this regard, a number of central nervous system mechanisms and neural pathways have emerged as crucial in chronically elevating SNA. However, it is important to consider that "sympathetic signatures" are present, with differential increases in SNA to regional organs that are dependent upon the disease progression. Here, we discuss recent findings on the central nervous system mechanisms and autonomic regulatory networks involved in neurogenic hypertension, in both non-obesity- and obesity-associated hypertension, with an emphasis on angiotensin-II, salt, oxidative and endoplasmic reticulum stress, inflammation, and the adipokine leptin. PMID:26957306

  3. Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems.

    PubMed

    Vrabie, Draguna; Lewis, Frank

    2009-04-01

    In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided. PMID:19362449

  4. Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

    PubMed

    Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

    2015-03-01

    This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique. PMID:25720014

  5. Control chart pattern recognition using K-MICA clustering and neural networks.

    PubMed

    Ebrahimzadeh, Ataollah; Addeh, Jalil; Rahmani, Zahra

    2012-01-01

    Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved. PMID:22035774

  6. Evaluation of neural network based real time maximum power tracking controller for PV system

    SciTech Connect

    Hiyama, Takashi; Kouzuma, Shinichi; Imakubo, Tomofumi; Ortmeyer, T.H.

    1995-09-01

    This paper presents a neural network based maximum power tracking controller for interconnected PV systems to commercial power sources. The neural network is utilized to identify the optimal operating voltage of the PV system. The controller generates the control signal in real time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV system to the identified optimal one, which yields the maximum power generation. The controller is a PI type one. The proportion an the integral gains are set to their optimal values to achieve the fast response and also to prevent the overshoot and also the undershoot. The continuous measurement is required for the open circuit voltage on the monitoring cell, and also for the terminal voltage of the PV system. Because of the accurate identification of the optimal operating voltage of the PV system, more than 99% power is drawn for the actual maximum power.

  7. Feedback linearisation control of an induction machine augmented by single-hidden layer neural networks

    NASA Astrophysics Data System (ADS)

    Ait Abbas, Hamou; Belkheiri, Mohammed; Zegnini, Boubakeur

    2016-01-01

    We consider adaptive output feedback control methodology of highly uncertain nonlinear systems with both parametric uncertainties and unmodelled dynamics. The approach is also applicable to systems of unknown, but bounded dimension. However, the relative degree of the regulated output is assumed to be known. This new control strategy is proposed to address the tracking problem of an induction motor based on a modified field-oriented control method. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modelling errors. The network weight adaptation rule is derived from the Lyapunov stability analysis, that guarantees boundedness of all the error signals of the closed-loop system. Computer simulations of an output feedback controlled induction machine, augmented via single-hidden-layer neural networks, demonstrate the practical potential of the proposed control algorithm.

  8. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    NASA Astrophysics Data System (ADS)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  9. Electron beam energy stabilization using a neural network hybrid controller at the Australian Synchrotron Linac.

    SciTech Connect

    Meier, E.; Morgan, M. J.; Biedron, S. G.; LeBlanc, G.; Wu, J.

    2009-01-01

    This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by the PI controller to attenuate the remaining perturbations. With a view to control the energy and bunch length at the FERMI{at}Elettra Free Electron Laser (FEL), the present study considers a neural network hybrid feed forward-feedback type of control to rectify limitations related to feedback systems, such as poor response for high jitter frequencies or limited bandwidth, while ensuring robustness of control. The Australian Synchrotron Linac is equipped with a beam position monitor (BPM), that was provided by Sincrotrone Trieste from a former transport line thus allowing energy measurements and energy control experiments. The present study will consequently focus on correcting energy jitter induced by variations in klystron phase and voltage.

  10. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    NASA Technical Reports Server (NTRS)

    Hageman, Jacob; Smith, Mark; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  11. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

    PubMed

    Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

    2016-02-01

    A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration. PMID:26336152

  12. A neural network approach to fault detection in spacecraft attitude determination and control systems

    NASA Astrophysics Data System (ADS)

    Schreiner, John N.

    This thesis proposes a method of performing fault detection and isolation in spacecraft attitude determination and control systems. The proposed method works by deploying a trained neural network to analyze a set of residuals that are defined such that they encompass the attitude control, guidance, and attitude determination subsystems. Eight neural networks were trained using either the resilient backpropagation, Levenberg-Marquardt, or Levenberg-Marquardt with Bayesian regularization training algorithms. The results of each of the neural networks were analyzed to determine the accuracy of the networks with respect to isolating the faulty component or faulty subsystem within the ADCS. The performance of the proposed neural network-based fault detection and isolation method was compared and contrasted with other ADCS FDI methods. The results obtained via simulation showed that the best neural networks employing this method successfully detected the presence of a fault 79% of the time. The faulty subsystem was successfully isolated 75% of the time and the faulty components within the faulty subsystem were isolated 37% of the time.

  13. Design, stability and robustness analyses of neural networks in control systems

    NASA Astrophysics Data System (ADS)

    Shen, Jie

    1998-12-01

    Artificial Neural Network (ANN), also known as connectionist learning and parallel distributed processing, is finding its applications in diverse fields: many branches of engineering, health sciences, cognitive science, archaeology, finance, etc. This research tries to make some efforts to emphasize "design" methodology in ANN, and to explore the structures by which ANN can solve difficult problems by identifying proper ANN architecture. Two classes of ANN--multi-layer neural networks and recurrent networks--are investigated in the context of control of systems and estimation of unknown parameters. The multi-layer neural networks converge to optimal solutions by satisfying mathematical formulations associated with the Hamilton approach and the dynamic programming approach. A benchmark aerospace application is used for illustration. A variant of the Hopfield network, called the Modified Hopfield Neural Network (MHNN), is proposed to show the design approach to the determination of weights in recurrent networks. It is shown how the equilibrium point of this network helps with inversion operations arising in optimal gain determination. Control of dynamic systems using recurrent neural networks are presented. The robustness of the recurrent networks to parameter variation is considered in the context of weights. Analyses are carried out in the frequency domain and the time domain.

  14. Neuroscience: A Distributed Neural Network Controls REM Sleep.

    PubMed

    Peever, John; Fuller, Patrick M

    2016-01-11

    How does the brain control dreams? New science shows that a small node of cells in the medulla - the most primitive part of the brain - may function to control REM sleep, the brain state that underlies dreaming. PMID:26766231

  15. Synchronization for an array of neural networks with hybrid coupling by a novel pinning control strategy.

    PubMed

    Gong, Dawei; Lewis, Frank L; Wang, Liping; Xu, Ke

    2016-05-01

    In this paper, a novel pinning synchronization (synchronization with pinning control) scheme for an array of neural networks with hybrid coupling is investigated. The main contributions are as follows: (1) A novel pinning control strategy is proposed for the first time. Pinning control schemes are introduced as an array of column vector. The controllers are designed as simple linear systems, which are easy to be analyzed or tested. (2) Augmented Lyapunov-Krasovskii functional (LKF) is applied to introduce more relax variables, which can alleviate the requirements of the positive definiteness of the matrix. (3) Based on the appropriate LKF, by introducing some free weighting matrices, some novel synchronization criteria are derived. Furthermore, the proposed pinning control scheme described by column vector can also be expanded to almost all the other array of neural networks. Finally, numerical examples are provided to show the effectiveness of the proposed results. PMID:26922719

  16. Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

    2016-02-01

    The study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.

  17. Atmospheric controls on Puerto Rico precipitation using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-01-01

    The growing need for local climate change scenarios has given rise to a wide range of empirical climate downscaling techniques. One of the most critical decisions in these methodologies is the selection of appropriate predictor variables for the downscaled surface predictand. A systematic approach to selecting predictor variables should be employed to ensure that the most important variables are utilized for the study site where the climate change scenarios are being developed. Tropical study areas have been far less examined than mid- and high-latitudes in the climate downscaling literature. As a result, studies analyzing optimal predictor variables for tropics are limited. The objectives of this study include developing artificial neural networks for six sites around Puerto Rico to develop nonlinear functions between 37 atmospheric predictor variables and local rainfall. The relative importance of each predictor is analyzed to determine the most important inputs in the network. Randomized ANNs are produced to determine the statistical significance of the relative importance of each predictor variable. Lower tropospheric moisture and winds are shown to be the most important variables at all sites. Results show inter-site variability in u- and v-wind importance depending on the unique geographic situation of the site. Lower tropospheric moisture and winds are physically linked to variability in sea surface temperatures (SSTs) and the strength and position of the North Atlantic High Pressure cell (NAHP). The changes forced by anthropogenic climate change in regional SSTs and the NAHP will impact rainfall variability in Puerto Rico.

  18. Embryonic cerebrospinal fluid in brain development: neural progenitor control.

    PubMed

    Gato, Angel; Alonso, M Isabel; Martín, Cristina; Carnicero, Estela; Moro, José Antonio; De la Mano, Aníbal; Fernández, José M F; Lamus, Francisco; Desmond, Mary E

    2014-08-28

    Due to the effort of several research teams across the world, today we have a solid base of knowledge on the liquid contained in the brain cavities, its composition, and biological roles. Although the cerebrospinal fluid (CSF) is among the most relevant parts of the central nervous system from the physiological point of view, it seems that it is not a permanent and stable entity because its composition and biological properties evolve across life. So, we can talk about different CSFs during the vertebrate life span. In this review, we focus on the CSF in an interesting period, early in vertebrate development before the formation of the choroid plexus. This specific entity is called "embryonic CSF." Based on the structure of the compartment, CSF composition, origin and circulation, and its interaction with neuroepithelial precursor cells (the target cells) we can conclude that embryonic CSF is different from the CSF in later developmental stages and from the adult CSF. This article presents arguments that support the singularity of the embryonic CSF, mainly focusing on its influence on neural precursor behavior during development and in adult life. PMID:25165044

  19. Embryonic cerebrospinal fluid in brain development: neural progenitor control

    PubMed Central

    Gato, Angel; Alonso, M. Isabel; Martín, Cristina; Carnicero, Estela; Moro, José Antonio; De la Mano, Aníbal; Fernández, José M. F.; Lamus, Francisco; Desmond, Mary E.

    2014-01-01

    Due to the effort of several research teams across the world, today we have a solid base of knowledge on the liquid contained in the brain cavities, its composition, and biological roles. Although the cerebrospinal fluid (CSF) is among the most relevant parts of the central nervous system from the physiological point of view, it seems that it is not a permanent and stable entity because its composition and biological properties evolve across life. So, we can talk about different CSFs during the vertebrate life span. In this review, we focus on the CSF in an interesting period, early in vertebrate development before the formation of the choroid plexus. This specific entity is called “embryonic CSF.” Based on the structure of the compartment, CSF composition, origin and circulation, and its interaction with neuroepithelial precursor cells (the target cells) we can conclude that embryonic CSF is different from the CSF in later developmental stages and from the adult CSF. This article presents arguments that support the singularity of the embryonic CSF, mainly focusing on its influence on neural precursor behavior during development and in adult life. PMID:25165044

  20. The Neural Basis of Sustained and Transient Attentional Control in Young Adults with ADHD

    ERIC Educational Resources Information Center

    Banich, Marie T.; Burgess, Gregory C.; Depue, Brendan E.; Ruzic, Luka; Bidwell, L. Cinnamon; Hitt-Laustsen, Sena; Du, Yiping P.; Willcutt, Erik G.

    2009-01-01

    Differences in neural activation during performance on an attentionally demanding Stroop task were examined between 23 young adults with ADHD carefully selected to not be co-morbid for other psychiatric disorders and 23 matched controls. A hybrid blocked/single-trial design allowed for examination of more sustained vs. more transient aspects of…

  1. Neural Mechanisms of Interference Control and Time Discrimination in Attention-Deficit/Hyperactivity Disorder

    ERIC Educational Resources Information Center

    Vloet, Timo D.; Gilsbach, Susanne; Neufang, Susanne; Fink, Gereon R.; Herpertz-Dahlmann, Beate; Konrad, Kerstin

    2010-01-01

    Objective: Both executive functions and time perception are typically impaired in subjects with attention-deficit/hyperactivity disorder (ADHD). However, the exact neural mechanisms underlying these deficits remain to be investigated. Method: Fourteen subjects with ADHD and 14 age- and IQ-matched controls (aged 9 through 15 years) were assessed…

  2. Neural Activation Underlying Cognitive Control in the Context of Neutral and Affectively Charged Pictures in Children

    ERIC Educational Resources Information Center

    Lamm, Connie; White, Lauren K.; McDermott, Jennifer Martin; Fox, Nathan A.

    2012-01-01

    The neural correlates of cognitive control for typically developing 9-year-old children were examined using dense-array ERPs and estimates of cortical activation (LORETA) during a go/no-go task with two conditions: a neutral picture condition and an affectively charged picture condition. Activation was estimated for the entire cortex after which…

  3. A Physiological Neural Controller of a Muscle Fiber Oculomotor Plant in Horizontal Monkey Saccades

    PubMed Central

    Enderle, John D.

    2014-01-01

    A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude. PMID:24944832

  4. Stem cell biology is population biology: differentiation of hematopoietic multipotent progenitors to common lymphoid and myeloid progenitors

    PubMed Central

    2013-01-01

    The hematopoietic stem cell (HSC) system is a demand control system, with the demand coming from the organism, since the products of the common myeloid and lymphoid progenitor (CMP, CLP respectively) cells are essential for activity and defense against disease. We show how ideas from population biology (combining population dynamics and evolutionary considerations) can illuminate the feedback control of the HSC system by the fully differentiated products, which has recently been verified experimentally. We develop models for the penultimate differentiation of HSC Multipotent Progenitors (MPPs) into CLP and CMP and introduce two concepts from population biology into stem cell biology. The first concept is the Multipotent Progenitor Commitment Response (MPCR) which is the probability that a multipotent progenitor cell follows a CLP route rather than a CMP route. The second concept is the link between the MPCR and a measure of Darwinian fitness associated with organismal performance and the levels of differentiated lymphoid and myeloid cells. We show that many MPCRs are consistent with homeostasis, but that they will lead to different dynamics of cells and signals following a wound or injury and thus have different consequences for Darwinian fitness. We show how coupling considerations of life history to dynamics of the HSC system and its products allows one to compute the selective pressures on cellular processes. We discuss ways that this framework can be used and extended. PMID:23327512

  5. Information content of neural networks with self-control and variable activity

    NASA Astrophysics Data System (ADS)

    Bollé, D.; Amari, S. I.; Dominguez Carreta, D. R. C.; Massolo, G.

    2001-02-01

    A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations.

  6. Neural interfaces for upper-limb prosthesis control: opportunities to improve long-term reliability.

    PubMed

    Judy, Jack W

    2012-03-01

    Building on a long history of innovation in neural-recording interfaces, the Defense Advanced Research Projects Agency (DARPA) has launched a program to address the key challenges related to transitioning advanced neuroprosthesis technology to clinical use for amputated service members. The goal of the Reliable Neural Technology (RE-NET) Program is to develop new technology to extract information from the nervous system at a scale and rate needed to reliably control modern robotic prostheses over the lifetime of the amputee. The RE-NET program currently encompasses three separate efforts: histology for interface stability over time (HIST), reliable peripheral interfaces (RPIs), and reliable central nervous system (CNS) interfaces (RCIs). PMID:22481748

  7. Functional Characteristics of Multipotent Mesenchymal Stromal Cells from Pituitary Adenomas

    PubMed Central

    Megnis, Kaspars; Mandrika, Ilona; Petrovska, Ramona; Stukens, Janis; Rovite, Vita; Balcere, Inga; Jansone, Laima Sabine; Peculis, Raitis; Pirags, Valdis

    2016-01-01

    Pituitary adenomas are one of the most common endocrine and intracranial neoplasms. Although they are theoretically monoclonal in origin, several studies have shown that they contain different multipotent cell types that are thought to play an important role in tumor initiation, maintenance, and recurrence after therapy. In the present study, we isolated and characterized cell populations from seven pituitary somatotroph, nonhormonal, and lactotroph adenomas. The obtained cells showed characteristics of multipotent mesenchymal stromal cells as observed by cell morphology, cell surface marker CD90, CD105, CD44, and vimentin expression, as well as differentiation to osteogenic and adipogenic lineages. They are capable of growth and passaging under standard laboratory cell culture conditions and do not manifest any hormonal cell characteristics. Multipotent mesenchymal stromal cells are present in pituitary adenomas regardless of their clinical manifestation and show no considerable expression of somatostatin 1–5 and dopamine 2 receptors. Most likely obtained cells are a part of tissue-supportive cells in pituitary adenoma microenvironment. PMID:27340409

  8. Functional Characteristics of Multipotent Mesenchymal Stromal Cells from Pituitary Adenomas.

    PubMed

    Megnis, Kaspars; Mandrika, Ilona; Petrovska, Ramona; Stukens, Janis; Rovite, Vita; Balcere, Inga; Jansone, Laima Sabine; Peculis, Raitis; Pirags, Valdis; Klovins, Janis

    2016-01-01

    Pituitary adenomas are one of the most common endocrine and intracranial neoplasms. Although they are theoretically monoclonal in origin, several studies have shown that they contain different multipotent cell types that are thought to play an important role in tumor initiation, maintenance, and recurrence after therapy. In the present study, we isolated and characterized cell populations from seven pituitary somatotroph, nonhormonal, and lactotroph adenomas. The obtained cells showed characteristics of multipotent mesenchymal stromal cells as observed by cell morphology, cell surface marker CD90, CD105, CD44, and vimentin expression, as well as differentiation to osteogenic and adipogenic lineages. They are capable of growth and passaging under standard laboratory cell culture conditions and do not manifest any hormonal cell characteristics. Multipotent mesenchymal stromal cells are present in pituitary adenomas regardless of their clinical manifestation and show no considerable expression of somatostatin 1-5 and dopamine 2 receptors. Most likely obtained cells are a part of tissue-supportive cells in pituitary adenoma microenvironment. PMID:27340409

  9. A hierarchical neural-network model for control and learning of voluntary movement.

    PubMed

    Kawato, M; Furukawa, K; Suzuki, R

    1987-01-01

    In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum--magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum--parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of

  10. Neural control of white, beige and brown adipocytes.

    PubMed

    Bartness, T J; Ryu, V

    2015-08-01

    Reports of brown-like adipocytes in traditionally white adipose tissue (WAT) depots occurred ~30 years ago, but interest in white adipocyte 'browning' only has gained attention more recently. We integrate some of what is known about the sympathetic nervous system (SNS) innervation of WAT and brown adipose tissue (BAT) with the few studies focusing on the sympathetic innervation of the so-called 'brite' or 'beige' adipocytes that appear when WAT sympathetic drive increases (for example, cold exposure and food deprivation). Only one brain site, the dorsomedial hypothalamic nucleus (DMH), selectively browns some (inguinal WAT (IWAT) and dorsomedial subcutaneous WAT), but not all WAT depots and only when DMH neuropeptide Y gene expression is knocked down, a browning effect is mediated by WAT SNS innervation. Other studies show that WAT sympathetic fiber density is correlated with the number of brown-like adipocytes (multilocular lipid droplets, uncoupling protein-1 immunoreactivity) at both warm and cold ambient temperatures. WAT and BAT have sensory innervation, the latter important for acute BAT cold-induced temperature increases, therefore suggesting the possible importance of sensory neural feedback from brite/beige cells for heat production. Only one report shows browned WAT capable of producing heat in vivo. Collectively, increases in WAT sympathetic drive and the phenotype of these stimulated adipocytes seems critical for the production of new and/or transdifferentiation of white to brite/beige adipocytes. Selective harnessing of WAT SNS drive to produce browning or selective browning independent of the SNS to counter increases in adiposity by increasing expenditure appears to be extremely challenging. PMID:27152173

  11. Neural control of white, beige and brown adipocytes

    PubMed Central

    Bartness, T J; Ryu, V

    2015-01-01

    Reports of brown-like adipocytes in traditionally white adipose tissue (WAT) depots occurred ~30 years ago, but interest in white adipocyte ‘browning' only has gained attention more recently. We integrate some of what is known about the sympathetic nervous system (SNS) innervation of WAT and brown adipose tissue (BAT) with the few studies focusing on the sympathetic innervation of the so-called ‘brite' or ‘beige' adipocytes that appear when WAT sympathetic drive increases (for example, cold exposure and food deprivation). Only one brain site, the dorsomedial hypothalamic nucleus (DMH), selectively browns some (inguinal WAT (IWAT) and dorsomedial subcutaneous WAT), but not all WAT depots and only when DMH neuropeptide Y gene expression is knocked down, a browning effect is mediated by WAT SNS innervation. Other studies show that WAT sympathetic fiber density is correlated with the number of brown-like adipocytes (multilocular lipid droplets, uncoupling protein-1 immunoreactivity) at both warm and cold ambient temperatures. WAT and BAT have sensory innervation, the latter important for acute BAT cold-induced temperature increases, therefore suggesting the possible importance of sensory neural feedback from brite/beige cells for heat production. Only one report shows browned WAT capable of producing heat in vivo. Collectively, increases in WAT sympathetic drive and the phenotype of these stimulated adipocytes seems critical for the production of new and/or transdifferentiation of white to brite/beige adipocytes. Selective harnessing of WAT SNS drive to produce browning or selective browning independent of the SNS to counter increases in adiposity by increasing expenditure appears to be extremely challenging. PMID:27152173

  12. Application of neural networks with orthogonal activation functions in control of dynamical systems

    NASA Astrophysics Data System (ADS)

    Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

    2016-04-01

    In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

  13. Neural-mechanical feedback control scheme generates physiological ankle torque fluctuation during quiet stance.

    PubMed

    Vette, Albert H; Masani, Kei; Nakazawa, Kimitaka; Popovic, Milos R

    2010-02-01

    We have recently demonstrated in simulations and experiments that a proportional and derivative (PD) feedback controller can regulate the active ankle torque during quiet stance and stabilize the body despite a long sensory-motor time delay. The purpose of the present study was to: 1) model the active and passive ankle torque mechanisms and identify their contributions to the total ankle torque during standing and 2) investigate whether a neural-mechanical control scheme that implements the PD controller as the neural controller can successfully generate the total ankle torque as observed in healthy individuals during quiet stance. Fourteen young subjects were asked to stand still on a force platform to acquire data for model optimization and validation. During two trials of 30 s each, the fluctuation of the body angle, the electromyogram of the right soleus muscle, and the ankle torque were recorded. Using these data, the parameters of: 1) the active and passive torque mechanisms (Model I) and 2) the PD controller within the neural-mechanical control scheme (Model II) were optimized to achieve potential matching between the measured and predicted ankle torque. The performance of the two models was finally validated with a new set of data. Our results indicate that not only the passive, but also the active ankle torque mechanism contributes significantly to the total ankle torque and, hence, to body stabilization during quiet stance. In addition, we conclude that the proposed neural-mechanical control scheme successfully mimics the physiological control strategy during quiet stance and that a PD controller is a legitimate model for the strategy that the central nervous system applies to regulate the active ankle torque in spite of a long sensory-motor time delay. PMID:20071280

  14. Neural adaptive control of nonlinear multivariable systems with application to a class of inverted pendulums.

    PubMed

    He, Shouling

    2002-10-01

    In this paper multilayer neural networks (MNNs) are used to control the balancing of a class of inverted pendulums. Unlike normal inverted pendulums, the pendulum discussed here has two degrees of rotational freedom and the base-point moves randomly in three-dimensional space. The goal is to apply control torques to keep the pendulum in a prescribed position in spite of the random movement at the base-point. Since the inclusion of the base-point motion leads to a non-autonomous dynamic system with time-varying parametric excitation, the design of the control system is a challenging task. A feedback control algorithm is proposed that utilizes a set of neural networks to compensate for the effect of the system's nonlinearities. The weight parameters of neural networks updated on-line, according to a learning algorithm that guarantees the Lyapunov stability of the control system. Furthermore, since the base-point movement is considered unmeasurable, a neural inverse model is employed to estimate it from only measured state variables. The estimate is then utilized within the main control algorithm to produce compensating control signals. The examination of the proposed control system, through simulations, demonstrates the promise of the methodology and exhibits positive aspects, which cannot be achieved by the previously developed techniques on the same problem. These aspects include fast, yet well-maintained damped responses with reasonable control torques and no requirement for knowledge of the model or the model parameters. The work presented here can benefit practical problems such as the study of stable locomotion of human upper body and bipedal robots. PMID:12424811

  15. Neural Control of Walking in People with Parkinsonism.

    PubMed

    Peterson, D S; Horak, F B

    2016-03-01

    People with Parkinson's disease exhibit debilitating gait impairments, including gait slowness, increased step variability, and poor postural control. A widespread supraspinal locomotor network including the cortex, cerebellum, basal ganglia, and brain stem contributes to the control of human locomotion, and altered activity of these structures underlies gait dysfunction due to Parkinson's disease. PMID:26889015

  16. Light-Activated Ion Channels for Remote Control of Neural Activity

    PubMed Central

    Chambers, James J.; Kramer, Richard H.

    2009-01-01

    Light-activated ion channels provide a new opportunity to precisely and remotely control neuronal activity for experimental applications in neurobiology. In the past few years, several strategies have arisen that allow light to control ion channels and therefore neuronal function. Light-based triggers for ion channel control include caged compounds, which release active neurotransmitters when photolyzed with light, and natural photoreceptive proteins, which can be expressed exogenously in neurons. More recently, a third type of light trigger has been introduced: a photoisomerizable tethered ligand that directly controls ion channel activity in a light-dependent manner. Beyond the experimental applications for light-gated ion channels, there may be clinical applications in which these light-sensitive ion channels could prove advantageous over traditional methods. Electrodes for neural stimulation to control disease symptoms are invasive and often difficult to reposition between cells in tissue. Stimulation by chemical agents is difficult to constrain to individual cells and has limited temporal accuracy in tissue due to diffusional limitations. In contrast, ion channels that can be directly activated with light allow control with unparalleled spatial and temporal precision. The goal of this chapter is to describe light-regulated ion channels and how they have been tailored to control different aspects of neural activity, and how to use these channels to manipulate and better understand development, function, and plasticity of neurons and neural circuits. PMID:19195553

  17. Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity.

    PubMed

    Liu, Zhi; Lai, Guanyu; Zhang, Yun; Chen, Chun Lung Philip

    2015-08-01

    This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper. PMID:25915964

  18. Neural-fuzzy controller for real-time mobile robot navigation

    NASA Astrophysics Data System (ADS)

    Ng, Kim C.; Trivedi, Mohan M.

    1996-06-01

    A neural integrated fuzzy controller (NiF-T), which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks, is developed for nonlinear dynamic control problems. The NiF-T architecture comprises three distinct parts: (1) fuzzy logic membership functions (FMF), (2) rule neural network (RNN), and (3) output-refinement neural network (ORNN). FMF are utilized to fuzzify input parameters. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. NiF-T can be applied for a wide range of sensor-driven robotics applications, which are characterized by high noise levels and nonlinear behavior, and where system models are unavailable or are unreliable. In this paper, real-time implementations of autonomous mobile robot navigation utilizing the NiF-T are realized. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. With learning capability, the robot, SMAR-T, successfully and reliably hugs wall, and locks onto hall center. For all of the described behaviors, their RNNs are trained only for a few hundred iterations and so are their ORNNs trained only for less than one hundred iterations to learn their parent rule sets.

  19. Adaptive Neural Control of MIMO Nonstrict-Feedback Nonlinear Systems With Time Delay.

    PubMed

    Zhao, Xudong; Yang, Haijiao; Karimi, Hamid Reza; Zhu, Yanzheng

    2016-06-01

    In this paper, an adaptive neural output-feedback tracking controller is designed for a class of multiple-input and multiple-output nonstrict-feedback nonlinear systems with time delay. The system coefficient and uncertain functions of our considered systems are both unknown. By employing neural networks to approximate the unknown function entries, and constructing a new input-driven filter, a backstepping design method of tracking controller is developed for the systems under consideration. The proposed controller can guarantee that all the signals in the closed-loop systems are ultimately bounded, and the time-varying target signal can be tracked within a small error as well. The main contributions of this paper lie in that the systems under consideration are more general, and an effective design procedure of output-feedback controller is developed for the considered systems, which is more applicable in practice. Simulation results demonstrate the efficiency of the proposed algorithm. PMID:26099151

  20. Robust adaptive neural control for a class of uncertain MIMO nonlinear systems

    NASA Astrophysics Data System (ADS)

    Wang, Chenliang; Lin, Yan

    2015-08-01

    In this paper, a novel robust adaptive neural control scheme is proposed for a class of uncertain multi-input multi-output nonlinear systems. The proposed scheme has the following main features: (1) a kind of Hurwitz condition is introduced to handle the state-dependent control gain matrix and some assumptions in existing schemes are relaxed; (2) by introducing a novel matrix normalisation technique, it is shown that all bound restrictions imposed on the control gain matrix in existing schemes can be removed; (3) the singularity problem is avoided without any extra effort, which makes the control law quite simple. Besides, with the aid of the minimal learning parameter technique, only one parameter needs to be updated online regardless of the system input-output dimension and the number of neural network nodes. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

  1. Evolution of an artificial neural network based autonomous land vehicle controller.

    PubMed

    Baluja, S

    1996-01-01

    This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks. PMID:18263046

  2. Two neural network algorithms for designing optimal terminal controllers with open final time

    NASA Technical Reports Server (NTRS)

    Plumer, Edward S.

    1992-01-01

    Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.

  3. Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

    PubMed Central

    Chen, Qihong; Long, Rong; Quan, Shuhai

    2014-01-01

    This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell. PMID:24707206

  4. [The influence of extreme factors on homing multipotent mesenchymal stromal cells].

    PubMed

    Maklakova, I Yu; Grebnev, Y D; Yastrebov, A P

    2015-01-01

    In this study, we studied homing multipotent mesenchymal stromal cells under influence of extreme factors: after radiation exposure, acute blood loss. Absorbed dose ionizing radiation amounted to 4.0 C (causes acute radiation sickness in mice), acute blood loss was caused by bleeding from the tail vein of the mouse in the amount of 2% of the body weight of the animal. Label MMSC used fluorochrome DAPI, ready to use. The experiments were performed on 60 Mature mice (males) age 6-8 months, weighing 20-25 g. Experiments on the culture of multipotent mesenchymal stromal cells from the placenta (chorion) performed on laboratory mice female at the age of 3-4 months in the gestation period of 14 days. Introduction suspensions of MMSC was carried out at a dose of 6 million cells/mouse, suspended in 0.2 ml 0.9% NaCl solution. The control group of laboratory animals MMSC transplantation was carried out also in the amount of 6 million cells/mouse. The assessment was made of tissue chimerism in the peripheral blood, bone marrow, spleen, small intestine, liver, lung, kidney, heart after 1 and 24 hours after transplantation of labeled cells. It was found a significant decrease in the content of labeled MMSC in the peripheral blood at extreme impact, indicating a migration of the transplanted cells in the damaged tissue. Homing transplanted MMSC is realized mainly in those tissues that underwent the most damage. PMID:27116883

  5. Artificial neural networks and approximate reasoning for intelligent control in space

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    A method is introduced for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement-learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. The model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. Some of the space domains suitable for applications of the model such as rendezvous and docking, camera tracking, and tethered systems control are discussed.

  6. Estimating Neural Background Input with Controlled and Fast Perturbations: A Bandwidth Comparison between Inhibitory Opsins and Neural Circuits.

    PubMed

    Eriksson, David

    2016-01-01

    To test the importance of a certain cell type or brain area it is common to make a "lack of function" experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 ms before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled "lack of function" experiments, but, it also suggests that such a "lack of function" experiment can be used to measure the context of local neural computations. PMID:27574506

  7. Controlling basins of attraction in a neural network-based telemetry monitor

    NASA Technical Reports Server (NTRS)

    Bell, Benjamin; Eilbert, James L.

    1988-01-01

    The size of the basins of attraction around fixed points in recurrent neural nets (NNs) can be modified by a training process. Controlling these attractive regions by presenting training data with various amount of noise added to the prototype signal vectors is discussed. Application of this technique to signal processing results in a classification system whose sensitivity can be controlled. This new technique is applied to the classification of temporal sequences in telemetry data.

  8. Neural control of lower urinary tract and targets for pharmacological therapy.

    PubMed

    Bortolini, Maria Augusta T; Bilhar, Andreisa P M; Castro, Rodrigo A

    2014-11-01

    Studies on the physiology and pharmacology of the lower urinary tract have brought new information and concepts about the complex neural control of micturition. There are many mechanisms, some proven and others not yet completely understood, in which pharmacological agents may act facilitating the filling, storage, and emptying of the bladder. This review describes the peripheral innervation and the main pathways involved in lower urinary tract control. It also presents potential targets for the treatment of voiding dysfunctions. PMID:25001574

  9. Neural signature of reward-modulated unconscious inhibitory control.

    PubMed

    Diao, Liuting; Qi, Senqing; Xu, Mengsi; Li, Zhiai; Ding, Cody; Chen, Antao; Zheng, Yan; Yang, Dong

    2016-09-01

    Consciously initiated cognitive control is generally determined by motivational incentives (e.g., monetary reward). Recent studies have revealed that human cognitive control processes can nevertheless operate without awareness. However, whether monetary reward can impinge on unconscious cognitive control remains unclear. To clarify this issue, a task consisting of several runs was designed to combine a modified version of the reward-priming paradigm with an unconscious version of the Go/No-Go task. At the beginning of each run, participants were exposed to a high- or low-value coin, followed by the modified Go/No-Go task. Participants could earn the coin only if they responded correctly to each trial of the run. Event-related potential (ERP) results indicated that high-value rewards (vs. low-value rewards) induced a greater centro-parietal P3 component associated with conscious and unconscious inhibitory control. Moreover, the P3 amplitude correlated positively with the magnitude of reaction time slowing reflecting the intensity of activation of unconscious inhibitory control in the brain. These findings suggest that high-value reward may facilitate human higher-order inhibitory processes that are independent of conscious awareness, which provides insights into the brain processes that underpin motivational modulation of cognitive control. PMID:27346057

  10. Consensus-based distributed cooperative learning from closed-loop neural control systems.

    PubMed

    Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

    2015-02-01

    In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper. PMID:25608294

  11. Neural Dysfunction in Cognitive Control Circuits in Persons at Clinical High-Risk for Psychosis.

    PubMed

    Colibazzi, Tiziano; Horga, Guillermo; Wang, Zhishun; Huo, Yuankai; Corcoran, Cheryl; Klahr, Kristin; Brucato, Gary; Girgis, Ragy; Gill, Kelly; Abi-Dargham, Anissa; Peterson, Bradley S

    2016-04-01

    Cognitive control, a set of functions that develop throughout adolescence, is important in the pathogenesis of psychotic disorders. Whether cognitive control has a role in conferring vulnerability for the development of psychotic illness is still unknown. The aim of this study was to investigate the neural systems supporting cognitive control in individuals deemed to be potentially prodromal for psychotic illness. We recruited 56 participants at clinical high-risk (CHR) for psychosis based on the Structured Interview for Psychosis-Risk Syndromes (SIPS) and 49 healthy controls. Twelve of the CHR participants eventually developed psychosis. We compared functional magnetic resonance imaging (fMRI) BOLD signal during the performance of the Simon task. We tested for differences between CHR individuals and controls in conflict-related functional activity. In the CHR group when compared with controls, we detected smaller conflict-related activations in several cortical areas, including the Dorsolateral Prefrontal Cortex (DLPFC). Furthermore, conflict-related activations in the DLPFC of those CHR individuals who ultimately developed psychosis (CHR converters) were smaller than in non-converters (CHR non-converters). Higher levels of conflict-related activation were associated with better social and role outcome. Risk for psychosis was associated at the neural level with reduced conflict-related brain activity. This neural phenotype appears correlated within the DLPFC with the development of psychosis and with functional outcome. PMID:26354046

  12. NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

    PubMed

    Vandewalle, Joos; De Moor, Bart L.R.; Suykens, Johan A.K.

    1997-06-01

    In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd. PMID:12662859

  13. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    PubMed

    Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

    2015-02-01

    This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research. PMID:25608292

  14. Neural emotion regulation circuitry underlying anxiolytic effects of perceived control over pain.

    PubMed

    Salomons, Tim V; Nusslock, Robin; Detloff, Allison; Johnstone, Tom; Davidson, Richard J

    2015-02-01

    Anxiolytic effects of perceived control have been observed across species. In humans, neuroimaging studies have suggested that perceived control and cognitive reappraisal reduce negative affect through similar mechanisms. An important limitation of extant neuroimaging studies of perceived control in terms of directly testing this hypothesis, however, is the use of within-subject designs, which confound participants' affective response to controllable and uncontrollable stress. To compare neural and affective responses when participants were exposed to either uncontrollable or controllable stress, two groups of participants received an identical series of stressors (thermal pain stimuli). One group ("controllable") was led to believe they had behavioral control over the pain stimuli, whereas another ("uncontrollable") believed they had no control. Controllable pain was associated with decreased state anxiety, decreased activation in amygdala, and increased activation in nucleus accumbens. In participants who perceived control over the pain, reduced state anxiety was associated with increased functional connectivity between each of these regions and ventral lateral/ventral medial pFC. The location of pFC findings is consistent with regions found to be critical for the anxiolytic effects of perceived control in rodents. Furthermore, interactions observed between pFC and both amygdala and nucleus accumbens are remarkably similar to neural mechanisms of emotion regulation through reappraisal in humans. These results suggest that perceived control reduces negative affect through a general mechanism involved in the cognitive regulation of emotion. PMID:25208742

  15. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

    NASA Astrophysics Data System (ADS)

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Black, Michael J.

    2008-12-01

    Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. Disclosure. JPD is the Chief Scientific Officer and a director of Cyberkinetics Neurotechnology Systems (CYKN); he holds stock and receives compensation. JDS has been a consultant for CYKN. LRH receives clinical trial support from CYKN.

  16. Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

    PubMed

    Colin, Guillaume; Chamaillard, Yann; Bloch, Gérard; Corde, Gilles

    2007-07-01

    Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods. PMID:17668664

  17. Adaptive neural network consensus based control of robot formations

    NASA Astrophysics Data System (ADS)

    Guzey, H. M.; Sarangapani, Jagannathan

    2013-05-01

    In this paper, adaptive consensus based formation control scheme is derived for mobile robots in a pre-defined formation when full dynamics of the robots which include inertia, Corolis, and friction vector are considered. It is shown that dynamic uncertainties of robots can make overall formation unstable when traditional consensus scheme is utilized. In order to estimate the affine nonlinear robot dynamics, a NN based adaptive scheme is utilized. In addition to this adaptive feedback control input, an additional control input is introduced based on the consensus approach to make the robots keep their desired formation. Subsequently, the outer consensus loop is redesigned for reduced communication. Lyapunov theory is used to show the stability of overall system. Simulation results are included at the end.

  18. A neural command circuit for grooming movement control.

    PubMed

    Hampel, Stefanie; Franconville, Romain; Simpson, Julie H; Seeds, Andrew M

    2015-01-01

    Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. Here we use anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, we find differences in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, we infer the possibility of a common circuit organization for movement control that can be dissected in Drosophila. PMID:26344548

  19. The hypoxia factor Hif-1α controls neural crest chemotaxis and epithelial to mesenchymal transition

    PubMed Central

    Barriga, Elias H.; Maxwell, Patrick H.

    2013-01-01

    One of the most important mechanisms that promotes metastasis is the stabilization of Hif-1 (hypoxia-inducible transcription factor 1). We decided to test whether Hif-1α also was required for early embryonic development. We focused our attention on the development of the neural crest, a highly migratory embryonic cell population whose behavior has been likened to cancer metastasis. Inhibition of Hif-1α by antisense morpholinos in Xenopus laevis or zebrafish embryos led to complete inhibition of neural crest migration. We show that Hif-1α controls the expression of Twist, which in turn represses E-cadherin during epithelial to mesenchymal transition (EMT) of neural crest cells. Thus, Hif-1α allows cells to initiate migration by promoting the release of cell–cell adhesions. Additionally, Hif-1α controls chemotaxis toward the chemokine SDF-1 by regulating expression of its receptor Cxcr4. Our results point to Hif-1α as a novel and key regulator that integrates EMT and chemotaxis during migration of neural crest cells. PMID:23712262

  20. Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control

    PubMed Central

    Vlachos, Ioannis; Deniz, Taşkin; Aertsen, Ad; Kumar, Arvind

    2016-01-01

    There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system. PMID:26829673

  1. Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control.

    PubMed

    Vlachos, Ioannis; Deniz, Taşkin; Aertsen, Ad; Kumar, Arvind

    2016-02-01

    There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system. PMID:26829673

  2. Circadian rhythm of temperature preference and its neural control in Drosophila

    PubMed Central

    Kaneko, Haruna; Head, Lauren M.; Ling, Jinli; Tang, Xin; Liu, Yilin; Hardin, Paul E.; Emery, Patrick; Hamada, Fumika N.

    2012-01-01

    A daily body temperature rhythm (BTR) is critical for the maintenance of homeostasis in mammals. While mammals use internal energy to regulate body temperature, ectotherms typically regulate body temperature behaviorally [1]. Some ectotherms maintain homeostasis via a daily temperature preference rhythm (TPR) [2], but the underlying mechanisms are largely unknown. Here, we show that Drosophila exhibit a daily circadian clock dependent TPR that resembles mammalian BTR. Pacemaker neurons critical for locomotor activity are not necessary for TPR; instead, the dorsal neuron 2s (DN2s), whose function was previously unknown, is sufficient. This indicates that TPR, like BTR, is controlled independently from locomotor activity. Therefore, the mechanisms controlling temperature fluctuations in fly TPR and mammalian BTR may share parallel features. Taken together, our results reveal the existence of a novel DN2- based circadian neural circuit that specifically regulates TPR; thus, understanding the mechanisms of TPR will shed new light on the function and neural control of circadian rhythms. PMID:22981774

  3. Neural computing for numeric-to-symbolic conversion in control systems

    NASA Technical Reports Server (NTRS)

    Passino, Kevin M.; Sartori, Michael A.; Antsaklis, Panos J.

    1989-01-01

    A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.

  4. Adaptive neural control for an uncertain robotic manipulator with joint space constraints

    NASA Astrophysics Data System (ADS)

    Tang, Zhong-Liang; Ge, Shuzhi Sam; Tee, Keng Peng; He, Wei

    2016-07-01

    In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.

  5. Neural Emotion Regulation Circuitry Underlying Anxiolytic Effects of Perceived Control Over Pain

    PubMed Central

    Salomons, Tim V.; Nusslock, Robin; Detloff, Allison; Johnstone, Tom; Davidson, Richard J.

    2014-01-01

    Anxiolytic effects of perceived control have been observed across species. In humans, neuroimaging studies have suggested that perceived control and cognitive reappraisal reduce negative affect through similar mechanisms. An important limitation of extant neuroimaging studies of perceived control in terms of directly testing this hypothesis, however, is the use of within subjects-designs, which confound participants' affective response to controllable and uncontrollable stress. To compare neural and affective responses when participants were exposed to either uncontrollable or controllable stress, two groups of participants received an identical series of stressors (thermal pain stimuli). One group (“controllable”) was led to believe they had behavioral control over the pain stimuli while another (“uncontrollable”) believed they had no control. Controllable pain was associated with decreased state anxiety, decreased activation in amygdala and increased activation in nucleus accumbens (NAcc). In participants who perceived control over the pain, reduced state anxiety was associated with increased functional connectivity between each of these regions and ventral lateral/ventral medial prefrontal cortex (PFC). The location of PFC findings is consistent with regions found to be critical for the anxiolytic effects of perceived control in rodents. Furthermore, interactions observed between PFC and both amygdala and NAcc are remarkably similar to neural mechanisms of emotion regulation through reappraisal in humans. These results suggest that perceived control reduces negative affect through a general mechanism involved in the cognitive regulation of emotion. PMID:25208742

  6. Neural network interactions and ingestive behavior control during anorexia.

    PubMed

    Watts, Alan G; Salter, Dawna S; Neuner, Christina M

    2007-07-24

    Many models have been proposed over the years to explain how motivated feeding behavior is controlled. One of the most compelling is based on the original concepts of Eliot Stellar whereby sets of interosensory and exterosensory inputs converge on a hypothalamic control network that can either stimulate or inhibit feeding. These inputs arise from information originating in the blood, the viscera, and the telencephalon. In this manner the relative strengths of the hypothalamic stimulatory and inhibitory networks at a particular time dictates how an animal feeds. Anorexia occurs when the balance within the networks consistently favors the restraint of feeding. This article discusses experimental evidence supporting a model whereby the increases in plasma osmolality that result from drinking hypertonic saline activate pathways projecting to neurons in the paraventricular nucleus of the hypothalamus (PVH) and lateral hypothalamic area (LHA). These neurons constitute the hypothalamic controller for ingestive behavior, and receive a set of afferent inputs from regions of the brain that process sensory information that is critical for different aspects of feeding. Important sets of inputs arise in the arcuate nucleus, the hindbrain, and in the telencephalon. Anorexia is generated in dehydrated animals by way of osmosensitive projections to the behavior control neurons in the PVH and LHA, rather than by actions on their afferent inputs. PMID:17531275

  7. Identification and control of plasma vertical position using neural network in Damavand tokamak

    SciTech Connect

    Rasouli, H.; Rasouli, C.; Koohi, A.

    2013-02-15

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  8. A direct self-constructing neural controller design for a class of nonlinear systems.

    PubMed

    Han, Honggui; Zhou, Wendong; Qiao, Junfei; Feng, Gang

    2015-06-01

    This paper is concerned with the problem of adaptive neural control for a class of uncertain or ill-defined nonaffine nonlinear systems. Using a self-organizing radial basis function neural network (RBFNN), a direct self-constructing neural controller (DSNC) is designed so that unknown nonlinearities can be approximated and the closed-loop system is stable. The key features of the proposed DSNC design scheme can be summarized as follows. First, different from the existing results in literature, a self-organizing RBFNN with adaptive threshold is constructed online for DSNC to improve the control performance. Second, the control law and adaptive law for the weights of RBFNN are established so that the closed-loop system is stable in the term of Lyapunov stability theory. Third, the tracking error is guaranteed to uniformly asymptotically converge to zero with the aid of an additional robustifying control term. An example is finally given to demonstrate the design procedure and the performance of the proposed method. Simulation results reveal the effectiveness of the proposed method. PMID:25706896

  9. Identification and control of plasma vertical position using neural network in Damavand tokamak.

    PubMed

    Rasouli, H; Rasouli, C; Koohi, A

    2013-02-01

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant. PMID:23464208

  10. Identification and control of plasma vertical position using neural network in Damavand tokamak

    NASA Astrophysics Data System (ADS)

    Rasouli, H.; Rasouli, C.; Koohi, A.

    2013-02-01

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  11. Closed loop adaptive control of spectrum-producing step using neural networks

    DOEpatents

    Fu, C.Y.

    1998-11-24

    Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.

  12. Closed loop adaptive control of spectrum-producing step using neural networks

    DOEpatents

    Fu, Chi Yung

    1998-01-01

    Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

  13. Primitive neural stem cells from the mammalian epiblast differentiate to definitive neural stem cells under the control of Notch signaling.

    PubMed

    Hitoshi, Seiji; Seaberg, Raewyn M; Koscik, Cheryl; Alexson, Tania; Kusunoki, Susumu; Kanazawa, Ichiro; Tsuji, Shoji; van der Kooy, Derek

    2004-08-01

    Basic fibroblast growth factor (FGF2)-responsive definitive neural stem cells first appear in embryonic day 8.5 (E8.5) mouse embryos, but not in earlier embryos, although neural tissue exists at E7.5. Here, we demonstrate that leukemia inhibitory factor-dependent (but not FGF2-dependent) sphere-forming cells are present in the earlier (E5.5-E7.5) mouse embryo. The resultant clonal sphere cells possess self-renewal capacity and neural multipotentiality, cardinal features of the neural stem cell. However, they also retain some nonneural properties, suggesting that they are the in vivo cells' equivalent of the primitive neural stem cells that form in vitro from embryonic stem cells. The generation of the in vivo primitive neural stem cell was independent of Notch signaling, but the activation of the Notch pathway was important for the transition from the primitive to full definitive neural stem cell properties and for the maintenance of the definitive neural stem cell state. PMID:15289455

  14. Shared neural processes support semantic control and action understanding

    PubMed Central

    Davey, James; Rueschemeyer, Shirley-Ann; Costigan, Alison; Murphy, Nik; Krieger-Redwood, Katya; Hallam, Glyn; Jefferies, Elizabeth

    2015-01-01

    Executive–semantic control and action understanding appear to recruit overlapping brain regions but existing evidence from neuroimaging meta-analyses and neuropsychology lacks spatial precision; we therefore manipulated difficulty and feature type (visual vs. action) in a single fMRI study. Harder judgements recruited an executive–semantic network encompassing medial and inferior frontal regions (including LIFG) and posterior temporal cortex (including pMTG). These regions partially overlapped with brain areas involved in action but not visual judgements. In LIFG, the peak responses to action and difficulty were spatially identical across participants, while these responses were overlapping yet spatially distinct in posterior temporal cortex. We propose that the co-activation of LIFG and pMTG allows the flexible retrieval of semantic information, appropriate to the current context; this might be necessary both for semantic control and understanding actions. Feature selection in difficult trials also recruited ventral occipital–temporal areas, not implicated in action understanding. PMID:25658631

  15. Neural control of glutamine synthetase activity in rat skeletal muscles.

    PubMed

    Feng, B; Konagaya, M; Konagaya, Y; Thomas, J W; Banner, C; Mill, J; Max, S R

    1990-05-01

    The mechanism of glutamine synthetase induction in rat skeletal muscle after denervation or limb immobilization was investigated. Adult male rats were subjected to midthigh section of the sciatic nerve. At 1, 2, and 5 h and 1, 2, and 7 days after denervation, rats were killed and denervated, and contralateral control soleus and plantaris muscles were excised, weighted, homogenized, and assayed for glutamine synthetase. Glutamine synthetase activity increased approximately twofold 1 h after denervation in both muscles. By 7 days postdenervation enzyme activity had increased to three times the control level in plantaris muscle and to four times the control level in soleus muscle. Increased enzyme activity after nerve section was associated with increased maximum velocity with no change in apparent Michaelis constant. Immunotitration with an antiglutamine synthetase antibody suggested that denervation caused an increase in the number of glutamine synthetase molecules in muscle. However, Northern-blot analysis revealed no increase in the steady-state level of glutamine synthetase mRNA after denervation. A mixing experiment failed to yield evidence for the presence of a soluble factor involved in regulating the activity of glutamine synthetase in denervated muscle. A combination of denervation and dexamethasone injections resulted in additive increases in glutamine synthetase. Thus the mechanism underlying increased glutamine synthetase after denervation appears to be posttranscriptional and is distinct from that of the glucocorticoid-mediated glutamine synthetase induction previously described by us. PMID:1970709

  16. A neural command circuit for grooming movement control

    PubMed Central

    Hampel, Stefanie; Franconville, Romain; Simpson, Julie H; Seeds, Andrew M

    2015-01-01

    Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. Here we use anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, we find differences in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, we infer the possibility of a common circuit organization for movement control that can be dissected in Drosophila. DOI: http://dx.doi.org/10.7554/eLife.08758.001 PMID:26344548

  17. Robust fuzzy neural network sliding mode control scheme for IPMSM drives

    NASA Astrophysics Data System (ADS)

    Leu, V. Q.; Mwasilu, F.; Choi, H. H.; Lee, J.; Jung, J. W.

    2014-07-01

    This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.

  18. Nonlinear model identification and adaptive model predictive control using neural networks.

    PubMed

    Akpan, Vincent A; Hassapis, George D

    2011-04-01

    This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time. PMID:21281932

  19. Truncated adaptation design for decentralised neural dynamic surface control of interconnected nonlinear systems under input saturation

    NASA Astrophysics Data System (ADS)

    Gao, Shigen; Dong, Hairong; Lyu, Shihang; Ning, Bin

    2016-07-01

    This paper studies decentralised neural adaptive control of a class of interconnected nonlinear systems, each subsystem is in the presence of input saturation and external disturbance and has independent system order. Using a novel truncated adaptation design, dynamic surface control technique and minimal-learning-parameters algorithm, the proposed method circumvents the problems of 'explosion of complexity' and 'dimension curse' that exist in the traditional backstepping design. Comparing to the methodology that neural weights are online updated in the controllers, only one scalar needs to be updated in the controllers of each subsystem when dealing with unknown systematic dynamics. Radial basis function neural networks (NNs) are used in the online approximation of unknown systematic dynamics. It is proved using Lyapunov stability theory that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. The tracking errors of each subsystems, the amplitude of NN approximation residuals and external disturbances can be attenuated to arbitrarily small by tuning proper design parameters. Simulation results are given to demonstrate the effectiveness of the proposed method.

  20. Epileptogenic zone localization and seizure control in coupled neural mass models.

    PubMed

    Ma, Zhen; Zhou, Weidong; Zhang, Yanli; Geng, Shujuan

    2015-12-01

    Exact localization of the epileptogenic zone (EZ) is the first priority for ensuring epilepsy treatments and reducing side effects. The results of traditional visual methods for localizing the origin of seizures are far from satisfactory in some cases. Signal processing methods could extract substantial information that may complement visual inspection of EEG signals. In this study, EZ localization is changed into a driver identification problem, and a nonlinear interdependence measure, the weighted rank interdependence, is proposed and used as a driver indicator because it can detect coupling information, especially directionality, from EEG signals. A proportional integral derivative (PID) controller is then explored, using simulations, to establish its suitability for seizure control. The seizure control we propose rests on identifying the EZ using nonlinear interdependence measures of directed functional connectivity. Two directionally coupled neural mass models are employed for simulation investigation. Two parameters can adjust the sensitivity and completeness of the weighted rank interdependence for different applications, and their effect is discussed in the context of neural mass models. Simulation results demonstrate that use of the weighted rank interdependence for EZ identification can be applied to different EZ types, and the approach achieves an overall identification rate of 98.84 % for several EZ types. Simulations also indicate that PID control can effectively regulate synchronization between neural masses. PMID:26585963

  1. fMRI of Simultaneous Interpretation Reveals the Neural Basis of Extreme Language Control.

    PubMed

    Hervais-Adelman, Alexis; Moser-Mercer, Barbara; Michel, Christoph M; Golestani, Narly

    2015-12-01

    We used functional magnetic resonance imaging (fMRI) to examine the neural basis of extreme multilingual language control in a group of 50 multilingual participants. Comparing brain responses arising during simultaneous interpretation (SI) with those arising during simultaneous repetition revealed activation of regions known to be involved in speech perception and production, alongside a network incorporating the caudate nucleus that is known to be implicated in domain-general cognitive control. The similarity between the networks underlying bilingual language control and general executive control supports the notion that the frequently reported bilingual advantage on executive tasks stems from the day-to-day demands of language control in the multilingual brain. We examined neural correlates of the management of simultaneity by correlating brain activity during interpretation with the duration of simultaneous speaking and hearing. This analysis showed significant modulation of the putamen by the duration of simultaneity. Our findings suggest that, during SI, the caudate nucleus is implicated in the overarching selection and control of the lexico-semantic system, while the putamen is implicated in ongoing control of language output. These findings provide the first clear dissociation of specific dorsal striatum structures in polyglot language control, roles that are consistent with previously described involvement of these regions in nonlinguistic executive control. PMID:25037924

  2. Application of neural network technology to fly-by-light control systems

    NASA Astrophysics Data System (ADS)

    Urnes, James M., Sr.

    1995-05-01

    McDonnell Douglas Aerospace (MDA) is developing a Neural Network based Intelligent Flight Control System that utilizes fly-by-light data transmission combined with high capacity flight processors to implement control advancements and fault monitoring processes. In this control system design, high speed data transfer and electromagnetic interference protection obtained through fiber optic technology is linked with Neural Network based flight control hardware processors that are programmed with damage adaptive control capability, thus providing maximum survivability for fighter aircraft. This system also provides enhanced component fault diagnostics that can identify subsystem failures during flight, thus providing reduced life cycle cost through efficient maintenance action and less downtime of the aircraft. The Intelligent Flight Control products apply to fighter and transport aircraft. This program is Task 2C of the ARPA Fly-by-Light Advanced Systems Hardware (FLASH) Technology Reinvestment Program. The principal partner with MDA for the Task 2C Intelligent Flight Control development is Martin Marietta Control Systems. The program will mature the system hardware and software for laboratory demonstrations of component fault diagnostics and highly adaptive flight control performance.

  3. Artificial neural networks: Principle and application to model based control of drying systems -- A review

    SciTech Connect

    Thyagarajan, T.; Ponnavaikko, M.; Shanmugam, J.; Panda, R.C.; Rao, P.G.

    1998-07-01

    This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

  4. Isolation, culture and analysis of adult subependymal neural stem cells.

    PubMed

    Belenguer, Germán; Domingo-Muelas, Ana; Ferrón, Sacri R; Morante-Redolat, José Manuel; Fariñas, Isabel

    2016-01-01

    Individual cells dissected from the subependymal neurogenic niche of the adult mouse brain proliferate in medium containing basic fibroblast growth factor (bFGF) and/or epidermal growth factor (EGF) as mitogens, to produce multipotent clonal aggregates called neurospheres. These cultures constitute a powerful tool for the study of neural stem cells (NSCs) provided that they allow the analysis of their features and potential capacity in a controlled environment that can be modulated and monitored more accurately than in vivo. Clonogenic and population analyses under mitogen addition or withdrawal allow the quantification of the self-renewing and multilineage potency of these cells and the identification of the mechanisms involved in these properties. Here, we describe a set of procedures developed and/or modified by our group including several experimental options that can be used either independently or in combination for the ex vivo assessment of cell properties of NSCs obtained from the adult subependymal niche. PMID:27016251

  5. Dynamic recurrent neural networks for stable adaptive control of wing rock motion

    NASA Astrophysics Data System (ADS)

    Kooi, Steven Boon-Lam

    Wing rock is a self-sustaining limit cycle oscillation (LCO) which occurs as the result of nonlinear coupling between the dynamic response of the aircraft and the unsteady aerodynamic forces. In this thesis, dynamic recurrent RBF (Radial Basis Function) network control methodology is proposed to control the wing rock motion. The concept based on the properties of the Presiach hysteresis model is used in the design of dynamic neural networks. The structure and memory mechanism in the Preisach model is analogous to the parallel connectivity and memory formation in the RBF neural networks. The proposed dynamic recurrent neural network has a feature for adding or pruning the neurons in the hidden layer according to the growth criteria based on the properties of ensemble average memory formation of the Preisach model. The recurrent feature of the RBF network deals with the dynamic nonlinearities and endowed temporal memories of the hysteresis model. The control of wing rock is a tracking problem, the trajectory starts from non-zero initial conditions and it tends to zero as time goes to infinity. In the proposed neural control structure, the recurrent dynamic RBF network performs identification process in order to approximate the unknown non-linearities of the physical system based on the input-output data obtained from the wing rock phenomenon. The design of the RBF networks together with the network controllers are carried out in discrete time domain. The recurrent RBF networks employ two separate adaptation schemes where the RBF's centre and width are adjusted by the Extended Kalman Filter in order to give a minimum networks size, while the outer networks layer weights are updated using the algorithm derived from Lyapunov stability analysis for the stable closed loop control. The issue of the robustness of the recurrent RBF networks is also addressed. The effectiveness of the proposed dynamic recurrent neural control methodology is demonstrated through simulations to

  6. Estimating Neural Background Input with Controlled and Fast Perturbations: A Bandwidth Comparison between Inhibitory Opsins and Neural Circuits

    PubMed Central

    Eriksson, David

    2016-01-01

    To test the importance of a certain cell type or brain area it is common to make a “lack of function” experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 ms before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled “lack of function” experiments, but, it also suggests that such a “lack of function” experiment can be used to measure the context of local neural computations. PMID:27574506

  7. Mutual information and self-control of a fully-connected low-activity neural network

    NASA Astrophysics Data System (ADS)

    Bollé, D.; Carreta, D. Dominguez

    2000-11-01

    A self-control mechanism for the dynamics of a three-state fully connected neural network is studied through the introduction of a time-dependent threshold. The self-adapting threshold is a function of both the neural and the pattern activity in the network. The time evolution of the order parameters is obtained on the basis of a recently developed dynamical recursive scheme. In the limit of low activity the mutual information is shown to be the relevant parameter in order to determine the retrieval quality. Due to self-control an improvement of this mutual information content as well as an increase of the storage capacity and an enlargement of the basins of attraction are found. These results are compared with numerical simulations.

  8. A neural network for controlling the configuration of frame structure with elastic members

    NASA Technical Reports Server (NTRS)

    Tsutsumi, Kazuyoshi

    1989-01-01

    A neural network for controlling the configuration of frame structure with elastic members is proposed. In the present network, the structure is modeled not by using the relative angles of the members but by using the distances between the joint locations alone. The relationship between the environment and the joints is also defined by their mutual distances. The analog neural network attains the reaching motion of the manipulator as a minimization problem of the energy constructed by the distances between the joints, the target, and the obstacles. The network can generate not only the final but also the transient configurations and the trajectory. This framework with flexibility and parallelism is very suitable for controlling the Space Telerobotic systems with many degrees of freedom.

  9. Neural Control of the Lower Urinary Tract: Peripheral and Spinal Mechanisms

    PubMed Central

    Birder, L.; de Groat, W.; Mills, I.; Morrison, J.; Thor, K.; Drake, M.

    2010-01-01

    This review deals with individual components regulating the neural control of the urinary bladder. This article will focus on factors and processes involved in the two modes of operation of the bladder: storage and elimination. Topics included in this review include: (1) The urothelium and its roles in sensor and transducer functions including interactions with other cell types within the bladder wall (“sensory web”), (2) The location and properties of bladder afferents including factors involved in regulating afferent sensitization, (3) The neural control of the pelvic floor muscle and pharmacology of urethral and anal sphincters (focusing on monoamine pathways), (4) Efferent pathways to the urinary bladder, and (5) Abnormalities in bladder function including mechanisms underlying comorbid disorders associated with bladder pain syndrome and incontinence. PMID:20025024

  10. Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

    PubMed Central

    Zhang, Fan; Liu, Ming; Harper, Stephen; Lee, Michael; Huang, He

    2014-01-01

    To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs. PMID:25079449

  11. A Central Neural Pathway Controlling Odor Tracking in Drosophila

    PubMed Central

    Slater, Gemma; Levy, Peter; Chan, K.L. Andrew

    2015-01-01

    Chemotaxis is important for the survival of most animals. How the brain translates sensory input into motor output beyond higher olfactory processing centers is largely unknown. We describe a group of excitatory neurons, termed Odd neurons, which are important for Drosophila larval chemotaxis. Odd neurons receive synaptic input from projection neurons in the calyx of the mushroom body and project axons to the central brain. Functional imaging shows that some of the Odd neurons respond to odor. Larvae in which Odd neurons are silenced are less efficient at odor tracking than controls and sample the odor space more frequently. Larvae in which the excitability of Odd neurons is increased are better at odor intensity discrimination and odor tracking. Thus, the Odd neurons represent a distinct pathway that regulates the sensitivity of the olfactory system to odor concentrations, demonstrating that efficient chemotaxis depends on processing of odor strength downstream of higher olfactory centers. PMID:25653345

  12. Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

    NASA Technical Reports Server (NTRS)

    Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

    2006-01-01

    The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

  13. Neural machine interfaces for controlling multifunctional powered upper-limb prostheses.

    PubMed

    Ohnishi, Kengo; Weir, Richard F; Kuiken, Todd A

    2007-01-01

    This article investigates various neural machine interfaces for voluntary control of externally powered upper-limb prostheses. Epidemiology of upper limb amputation, as well as prescription and follow-up studies of externally powered upper-limb prostheses are discussed. The use of electromyographic interfaces and peripheral nerve interfaces for prosthetic control, as well as brain machine interfaces suitable for prosthetic control, are examined in detail along with available clinical results. In addition, studies on interfaces using muscle acoustic and mechanical properties and the problem of interfacing sensory information to the nervous system are discussed. PMID:17187470

  14. Intelligent control of robotic arm/hand systems for the NASA EVA retriever using neural networks

    NASA Technical Reports Server (NTRS)

    Mclauchlan, Robert A.

    1989-01-01

    Adaptive/general learning algorithms using varying neural network models are considered for the intelligent control of robotic arm plus dextrous hand/manipulator systems. Results are summarized and discussed for the use of the Barto/Sutton/Anderson neuronlike, unsupervised learning controller as applied to the stabilization of an inverted pendulum on a cart system. Recommendations are made for the application of the controller and a kinematic analysis for trajectory planning to simple object retrieval (chase/approach and capture/grasp) scenarios in two dimensions.

  15. Chaos control and synchronization, with input saturation, via recurrent neural networks.

    PubMed

    Sanchez, Edgar N; Ricalde, Luis J

    2003-01-01

    This paper deals with the adaptive tracking problem of non-linear systems in presence of unknown parameters, unmodelled dynamics and input saturation. A high order recurrent neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then a stabilizing control law for the reference tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law. Tracking error boundedness is established as a function of a design parameter. The new approach is illustrated by examples of complex dynamical systems: chaos control and synchronization. PMID:12850026

  16. Applying backpropagation neural network in the control of medullary reflex pattern

    NASA Astrophysics Data System (ADS)

    Dalcin, Bruno Luiz Galluzzi; Cruz, Frederico Alan de Oliveira; Cortez, Célia Martins; Passos, Emmanuel Lopes

    2015-12-01

    We introduced in an artificial neural network (ANN) values of the data matrix that was built with results from simulations performed with the model for the control circuit of spinal reflex presented by Dalcin et al. (2005). Standard multi-layered feed-forward backpropagation network was used to train the ANNs. Results showed that the backpropagation ANN architecture supported the specific classificatory requirements of the study.

  17. [Therapeutic Effects of Multipotent Mesenchymal Stromal Cells after Irradiation].

    PubMed

    Kalmykova, N V; Alexandrova, S A

    2016-01-01

    Multipotent mesenchymal stromal cells (MSC) are now considered to be a perspective multifunctional treatment option for radiation side effects. At present.a great number of sufficient evidence has been collected in favor of therapeutic effects of MSCs in acute radiation reactions. It has been shown that MSC-based products injected locally or systemically have therapeutic effects on irradiated organs and tissues. This review presents summarized experimental and clinical data about protective and regenerative effects of MSCs on different radiation-injured organs and tissues; the main probable therapeutic mechanisms of their action are also discussed. PMID:27534063

  18. Integrated Neural and Endocrine Control of Gastrointestinal Function.

    PubMed

    Furness, John B

    2016-01-01

    The activity of the digestive system is dynamically regulated by external factors, including body nutritional and activity states, emotions and the contents of the digestive tube. The gut must adjust its activity to assimilate a hugely variable mixture that is ingested, particularly in an omnivore such as human for which a wide range of food choices exist. It must also guard against toxins and pathogens. These nutritive and non-nutritive components of the gut contents interact with the largest and most vulnerable surface in the body, the lining of the gastrointestinal tract. This requires a gut sensory system that can detect many classes of nutrients, non-nutrient components of food, physicochemical conditions, toxins, pathogens and symbionts (Furness et al., Nat Rev Gastroenterol Hepatol 10:729-740, 2013). The gut sensors are in turn coupled to effector systems that can respond to the sensory information. The responses are exerted through enteroendocrine cells (EEC), the enteric nervous system (ENS), the central nervous system (CNS) and the gut immune and tissue defence systems. It is apparent that the control of the digestive organs is an integrated function of these effectors. The peripheral components of the EEC, ENS and CNS triumvirate are extensive. EEC cells have traditionally been classified into about 12 types (disputed in this review), releasing about 20 hormones, together making the gut endocrine system the largest endocrine organ in the body. Likewise, in human the ENS contains about 500 million neurons, far more than the number of neurons in the remainder of the peripheral autonomic nervous system. Together gut hormones, the ENS and the CNS control or influence functions including satiety, mixing and propulsive activity, release of digestive enzymes, induction of nutrient transporters, fluid transport, local blood flow, gastric acid secretion, evacuation and immune responses. Gut content receptors, including taste, free fatty acid, peptide and

  19. Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems.

    PubMed

    Shahriari kahkeshi, Maryam; Sheikholeslam, Farid; Zekri, Maryam

    2013-05-01

    This paper proposes novel adaptive fuzzy wavelet neural sliding mode controller (AFWN-SMC) for a class of uncertain nonlinear systems. The main contribution of this paper is to design smooth sliding mode control (SMC) for a class of high-order nonlinear systems while the structure of the system is unknown and no prior knowledge about uncertainty is available. The proposed scheme composed of an Adaptive Fuzzy Wavelet Neural Controller (AFWNC) to construct equivalent control term and an Adaptive Proportional-Integral (A-PI) controller for implementing switching term to provide smooth control input. Asymptotical stability of the closed loop system is guaranteed, using the Lyapunov direct method. To show the efficiency of the proposed scheme, some numerical examples are provided. To validate the results obtained by proposed approach, some other methods are adopted from the literature and applied for comparison. Simulation results show superiority and capability of the proposed controller to improve the steady state performance and transient response specifications by using less numbers of fuzzy rules and on-line adaptive parameters in comparison to other methods. Furthermore, control effort has considerably decreased and chattering phenomenon has been completely removed. PMID:23453235

  20. Cognitive Control in Adolescence: Neural Underpinnings and Relation to Self-Report Behaviors

    PubMed Central

    Andrews-Hanna, Jessica R.; Mackiewicz Seghete, Kristen L.; Claus, Eric D.; Burgess, Gregory C.; Ruzic, Luka; Banich, Marie T.

    2011-01-01

    Background Adolescence is commonly characterized by impulsivity, poor decision-making, and lack of foresight. However, the developmental neural underpinnings of these characteristics are not well established. Methodology/Principal Findings To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI) Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution. Conclusions/Significance Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors. PMID:21738725

  1. How do negative emotions impair self-control? A neural model of negative urgency.

    PubMed

    Chester, David S; Lynam, Donald R; Milich, Richard; Powell, David K; Andersen, Anders H; DeWall, C Nathan

    2016-05-15

    Self-control often fails when people experience negative emotions. Negative urgency represents the dispositional tendency to experience such self-control failure in response to negative affect. Neither the neural underpinnings of negative urgency nor the more general phenomenon of self-control failure in response to negative emotions are fully understood. Previous theorizing suggests that an insufficient, inhibitory response from the prefrontal cortex may be the culprit behind such self-control failure. However, we entertained an alternative hypothesis: negative emotions lead to self-control failure because they excessively tax inhibitory regions of the prefrontal cortex. Using fMRI, we compared the neural activity of people high in negative urgency with controls on an emotional, inhibitory Go/No-Go task. While experiencing negative (but not positive or neutral) emotions, participants high in negative urgency showed greater recruitment of inhibitory brain regions than controls. Suggesting a compensatory function, inhibitory accuracy among participants high in negative urgency was associated with greater prefrontal recruitment. Greater activity in the anterior insula on negatively-valenced, inhibitory trials predicted greater substance abuse one month and one year after the MRI scan among individuals high in negative urgency. These results suggest that, among people whose negative emotions often lead to self-control failure, excessive reactivity of the brain's regulatory resources may be the culprit. PMID:26892861

  2. Polyphenols: Multipotent Therapeutic Agents in Neurodegenerative Diseases

    PubMed Central

    Bhullar, Khushwant S.; Rupasinghe, H. P. Vasantha

    2013-01-01

    Aging leads to numerous transitions in brain physiology including synaptic dysfunction and disturbances in cognition and memory. With a few clinically relevant drugs, a substantial portion of aging population at risk for age-related neurodegenerative disorders require nutritional intervention. Dietary intake of polyphenols is known to attenuate oxidative stress and reduce the risk for related neurodegenerative diseases such as Alzheimer's disease (AD), stroke, multiple sclerosis (MS), Parkinson's disease (PD), and Huntington's disease (HD). Polyphenols exhibit strong potential to address the etiology of neurological disorders as they attenuate their complex physiology by modulating several therapeutic targets at once. Firstly, we review the advances in the therapeutic role of polyphenols in cell and animal models of AD, PD, MS, and HD and activation of drug targets for controlling pathological manifestations. Secondly, we present principle pathways in which polyphenol intake translates into therapeutic outcomes. In particular, signaling pathways like PPAR, Nrf2, STAT, HIF, and MAPK along with modulation of immune response by polyphenols are discussed. Although current polyphenol researches have limited impact on clinical practice, they have strong evidence and testable hypothesis to contribute clinical advances and drug discovery towards age-related neurological disorders. PMID:23840922

  3. Inhibition and impulsivity: behavioral and neural basis of response control.

    PubMed

    Bari, Andrea; Robbins, Trevor W

    2013-09-01

    In many circumstances alternative courses of action and thoughts have to be inhibited to allow the emergence of goal-directed behavior. However, this has not been the accepted view in the past and only recently has inhibition earned its own place in the neurosciences as a fundamental cognitive function. In this review we first introduce the concept of inhibition from early psychological speculations based on philosophical theories of the human mind. The broad construct of inhibition is then reduced to its most readily observable component which necessarily is its behavioral manifestation. The study of 'response inhibition' has the advantage of dealing with a relatively simple and straightforward process, the overriding of a planned or already initiated action. Deficient inhibitory processes profoundly affect everyday life, causing impulsive conduct which is generally detrimental for the individual. Impulsivity has been consistently linked to several types of addiction, attention deficit/hyperactivity disorder, mania and other psychiatric conditions. Our discussion of the behavioral assessment of impulsivity will focus on objective laboratory tasks of response inhibition that have been implemented in parallel for humans and other species with relatively few qualitative differences. The translational potential of these measures has greatly improved our knowledge of the neurobiological basis of behavioral inhibition and impulsivity. We will then review the current models of behavioral inhibition along with their expression via underlying brain regions, including those involved in the activation of the brain's emergency 'brake' operation, those engaged in more controlled and sustained inhibitory processes and other ancillary executive functions. PMID:23856628

  4. Neural Control of Blood Pressure in Chronic Intermittent Hypoxia.

    PubMed

    Shell, Brent; Faulk, Katelynn; Cunningham, J Thomas

    2016-03-01

    Sleep apnea (SA) is increasing in prevalence and is commonly comorbid with hypertension. Chronic intermittent hypoxia is used to model the arterial hypoxemia seen in SA, and through this paradigm, the mechanisms that underlie SA-induced hypertension are becoming clear. Cyclic hypoxic exposure during sleep chronically stimulates the carotid chemoreflexes, inducing sensory long-term facilitation, and drives sympathetic outflow from the hindbrain. The elevated sympathetic tone drives hypertension and renal sympathetic activity to the kidneys resulting in increased plasma renin activity and eventually angiotensin II (Ang II) peripherally. Upon waking, when respiration is normalized, the sympathetic activity does not diminish. This is partially because of adaptations leading to overactivation of the hindbrain regions controlling sympathetic outflow such as the nucleus tractus solitarius (NTS), and rostral ventrolateral medulla (RVLM). The sustained sympathetic activity is also due to enhanced synaptic signaling from the forebrain through the paraventricular nucleus (PVN). During the waking hours, when the chemoreceptors are not exposed to hypoxia, the forebrain circumventricular organs (CVOs) are stimulated by peripherally circulating Ang II from the elevated plasma renin activity. The CVOs and median preoptic nucleus chronically activate the PVN due to the Ang II signaling. All together, this leads to elevated nocturnal mean arterial pressure (MAP) as a response to hypoxemia, as well as inappropriately elevated diurnal MAP in response to maladaptations. PMID:26838032

  5. Central neural control of the cardiovascular system: current perspectives.

    PubMed

    Dampney, Roger A L

    2016-09-01

    This brief review, which is based on a lecture presented at the American Physiological Society Teaching Refresher Course on the Brain and Systems Control as part of the Experimental Biology meeting in 2015, aims to summarize current concepts of the principal mechanisms in the brain that regulate the autonomic outflow to the cardiovascular system. Such cardiovascular regulatory mechanisms do not operate in isolation but are closely coordinated with respiratory and other regulatory mechanisms to maintain homeostasis. The brain regulates the cardiovascular system by two general means: 1) feedforward regulation, often referred to as "central command," and 2) feedback or reflex regulation. In most situations (e.g., during exercise, defensive behavior, sleep, etc.), both of these general mechanisms contribute to overall cardiovascular homeostasis. The review first describes the mechanisms and central circuitry subserving the baroreceptor, chemoreceptor, and other reflexes that work together to regulate an appropriate level of blood pressure and blood oxygenation and then considers the brain mechanisms that defend the body against more complex environmental challenges, using dehydration and cold and heat stress as examples. The last section of the review considers the central mechanisms regulating cardiovascular function associated with different behaviors, with a specific focus on defensive behavior and exercise. PMID:27445275

  6. Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network

    NASA Astrophysics Data System (ADS)

    Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao

    2009-10-01

    A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.

  7. Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

    NASA Astrophysics Data System (ADS)

    Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

    2016-09-01

    The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

  8. Falcon: neural fuzzy control and decision systems using FKP and PFKP clustering algorithms.

    PubMed

    Tung, W L; Quek, C

    2004-02-01

    Neural fuzzy networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. Examples are the Falcon-ART, and the POPFNN family of networks. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. This correspondence proposes two new networks: Falcon-FKP and Falcon-PFKP. They are extensions of the Falcon-ART network, and aimed to overcome the shortcomings faced by the Falcon-ART network itself, i.e., poor classification ability when the classes of input data are very similar to each other, termination of training cycle depends heavily on a preset error parameter, the fuzzy rule base of the Falcon-ART network may not be consistent Nauck, there is no control over the number of fuzzy rules generated, and learning efficiency may deteriorate by using complementarily coded training data. These deficiencies are essentially inherent to the fuzzy ART, clustering technique employed by the Falcon-ART network. Hence, two clustering techniques--Fuzzy Kohonen Partitioning (FKP) and its pseudo variant PFKP, are synthesized with the basic Falcon structure to compute the fuzzy sets and to automatically derive the fuzzy rules from the training data. The resultant neural fuzzy networks are Falcon-FKP and Falcon-PFKP, respectively. These two proposed networks have a lean and efficient training algorithm and consistent fuzzy rule bases. Extensive simulations are conducted using the two networks and their performances are encouraging when benchmarked against other neural and neural fuzzy systems. PMID:15369109

  9. Output feedback direct adaptive neural network control for uncertain SISO nonlinear systems using a fuzzy estimator of the control error.

    PubMed

    Chemachema, Mohamed

    2012-12-01

    A direct adaptive control algorithm, based on neural networks (NN) is presented for a class of single input single output (SISO) nonlinear systems. The proposed controller is implemented without a priori knowledge of the nonlinear systems; and only the output of the system is considered available for measurement. Contrary to the approaches available in the literature, in the proposed controller, the updating signal used in the adaptive laws is an estimate of the control error, which is directly related to the NN weights instead of the tracking error. A fuzzy inference system (FIS) is introduced to get an estimate of the control error. Without any additional control term to the NN adaptive controller, all the signals involved in the closed loop are proven to be exponentially bounded and hence the stability of the system. Simulation results demonstrate the effectiveness of the proposed approach. PMID:23037773

  10. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    NASA Technical Reports Server (NTRS)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-01-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  11. Parietal Neural Prosthetic Control of a Computer Cursor in a Graphical-User-Interface Task

    PubMed Central

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A

    2014-01-01

    Objective To date, the majority of Brain Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in Area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like “Face in a Crowd” task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the “Crowd”) using a neurally controlled cursor. We assessed whether the Crowd affected decodes of intended cursor movements by comparing it to a “Crowd Off” condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main Results Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the Crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  12. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

    NASA Astrophysics Data System (ADS)

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

    2014-12-01

    Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  13. Exponential Stabilization of Memristor-based Chaotic Neural Networks with Time-Varying Delays via Intermittent Control.

    PubMed

    Zhang, Guodong; Shen, Yi

    2015-07-01

    This paper is concerned with the global exponential stabilization of memristor-based chaotic neural networks with both time-varying delays and general activation functions. Here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new sufficient conditions ensuring exponential stabilization of memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the proposed results here are easy to verify and they also extend the earlier publications. Finally, numerical simulations illustrate the effectiveness of the obtained results. PMID:25148672

  14. Insulin–InsR signaling drives multipotent progenitor differentiation toward lymphoid lineages

    PubMed Central

    Xia, Pengyan; Wang, Shuo; Du, Ying; Huang, Guanling; Satoh, Takashi; Akira, Shizuo

    2015-01-01

    The lineage commitment of HSCs generates balanced myeloid and lymphoid populations in hematopoiesis. However, the underlying mechanisms that control this process remain largely unknown. Here, we show that insulin–insulin receptor (InsR) signaling is required for lineage commitment of multipotent progenitors (MPPs). Deletion of Insr in murine bone marrow causes skewed differentiation of MPPs to myeloid cells. mTOR acts as a downstream effector that modulates MPP differentiation. mTOR activates Stat3 by phosphorylation at serine 727 under insulin stimulation, which binds to the promoter of Ikaros, leading to its transcription priming. Our findings reveal that the insulin–InsR signaling drives MPP differentiation into lymphoid lineages in early lymphopoiesis, which is essential for maintaining a balanced immune system for an individual organism. PMID:26573296

  15. Back propagation neural network based control for the heating system of a polysilicon reduction furnace

    NASA Astrophysics Data System (ADS)

    Cheng, Yuhua; Chen, Kai; Bai, Libing; Dai, Meizhi

    2013-12-01

    In this paper, the Back Propagation (BP) neural network based control strategy is proposed for the heating system of a polysilicon reduction furnace. It is applied to obtain the control signal Id, which is used to adjust the heating power through operations of the silicon core temperature, furnace temperature, silicon core voltage, and resistance of the current control cycle. With the control signal Id the polycrystalline silicon can be heated from room temperature to the required temperature smoothly and steadily. The proposed BP network applied in this paper can obtain the accurate control signal Id and achieve the precise control purpose. This paper presents the principle of the BP network and demonstrates the effectiveness of the BP network in the heating system of a polysilicon reduction furnace by combining the simulation analysis with experimental results.

  16. Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

    NASA Astrophysics Data System (ADS)

    Zhao, Lin; Jia, Yingmin

    2016-06-01

    In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

  17. Characteristics and multipotency of equine dedifferentiated fat cells

    PubMed Central

    MURATA, Daiki; YAMASAKI, Atsushi; MATSUZAKI, Shouta; SUNAGA, Takafumi; FUJIKI, Makoto; TOKUNAGA, Satoshi; MISUMI, Kazuhiro

    2016-01-01

    ABSTRACT Dedifferentiated fat (DFAT) cells have been shown to be multipotent, similar to mesenchymal stem cells (MSCs). In this study, we aimed to establish and characterize equine DFAT cells. Equine adipocytes were ceiling cultured, and then dedifferentiated into DFAT cells by the seventh day of culture. The number of DFAT cells was increased to over 10 million by the fourth passage. Flow cytometry of DFAT cells showed that the cells were strongly positive for CD44, CD90, and major histocompatibility complex (MHC) class I; moderately positive for CD11a/18, CD105, and MHC class II; and negative for CD34 and CD45. Moreover, DFAT cells were positive for the expression of sex determining region Y-box 2 as a marker of multipotency. Finally, we found that DFAT cells could differentiate into osteogenic, chondrogenic, and adipogenic lineages under specific nutrient conditions. Thus, DFAT cells could have clinical applications in tissue regeneration, similar to MSCs derived from adipose tissue. PMID:27330399

  18. Lactic Acid Bacteria Convert Human Fibroblasts to Multipotent Cells

    PubMed Central

    Ohta, Kunimasa; Kawano, Rie; Ito, Naofumi

    2012-01-01

    The human gastrointestinal tract is colonized by a vast community of symbionts and commensals. Lactic acid bacteria (LAB) form a group of related, low-GC-content, gram-positive bacteria that are considered to offer a number of probiotic benefits to general health. While the role of LAB in gastrointestinal microecology has been the subject of extensive study, little is known about how commensal prokaryotic organisms directly influence eukaryotic cells. Here, we demonstrate the generation of multipotential cells from adult human dermal fibroblast cells by incorporating LAB. LAB-incorporated cell clusters are similar to embryoid bodies derived from embryonic stem cells and can differentiate into endodermal, mesodermal, and ectodermal cells in vivo and in vitro. LAB-incorporated cell clusters express a set of genes associated with multipotency, and microarray analysis indicates a remarkable increase of NANOG, a multipotency marker, and a notable decrease in HOX gene expression in LAB-incorporated cells. During the cell culture, the LAB-incorporated cell clusters stop cell division and start to express early senescence markers without cell death. Thus, LAB-incorporated cell clusters have potentially wide-ranging implications for cell generation, reprogramming, and cell-based therapy. PMID:23300571

  19. Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks.

    PubMed

    Chansanroj, Krisanin; Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele

    2011-10-01

    Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties. PMID:21878388

  20. Nuclear receptor NR5A2 controls neural stem cell fate decisions during development

    PubMed Central

    Stergiopoulos, Athanasios; Politis, Panagiotis K.

    2016-01-01

    The enormous complexity of mammalian central nervous system (CNS) is generated by highly synchronized actions of diverse factors and signalling molecules in neural stem/progenitor cells (NSCs). However, the molecular mechanisms that integrate extrinsic and intrinsic signals to control proliferation versus differentiation decisions of NSCs are not well-understood. Here we identify nuclear receptor NR5A2 as a central node in these regulatory networks and key player in neural development. Overexpression and loss-of-function experiments in primary NSCs and mouse embryos suggest that NR5A2 synchronizes cell-cycle exit with induction of neurogenesis and inhibition of astrogliogenesis by direct regulatory effects on Ink4/Arf locus, Prox1, a downstream target of proneural genes, as well as Notch1 and JAK/STAT signalling pathways. Upstream of NR5a2, proneural genes, as well as Notch1 and JAK/STAT pathways control NR5a2 endogenous expression. Collectively, these observations render NR5A2 a critical regulator of neural development and target gene for NSC-based treatments of CNS-related diseases. PMID:27447294

  1. Dynamic neural networks based on-line identification and control of high performance motor drives

    NASA Technical Reports Server (NTRS)

    Rubaai, Ahmed; Kotaru, Raj

    1995-01-01

    In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

  2. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    PubMed

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform. PMID:16859445

  3. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    NASA Astrophysics Data System (ADS)

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

    2011-01-01

    Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  4. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    NASA Astrophysics Data System (ADS)

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

    2010-09-01

    Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  5. Finite-Time Stabilizability and Instabilizability of Delayed Memristive Neural Networks With Nonlinear Discontinuous Controller.

    PubMed

    Wang, Leimin; Shen, Yi

    2015-11-01

    This paper is concerned about the finite-time stabilizability and instabilizability for a class of delayed memristive neural networks (DMNNs). Through the design of a new nonlinear controller, algebraic criteria based on M -matrix are established for the finite-time stabilizability of DMNNs, and the upper bound of the settling time for stabilization is estimated. In addition, finite-time instabilizability algebraic criteria are also established by choosing different parameters of the same nonlinear controller. The effectiveness and the superiority of the obtained results are supported by numerical simulations. PMID:26277003

  6. GABA's Control of Stem and Cancer Cell Proliferation in Adult Neural and Peripheral Niches

    PubMed Central

    Young, Stephanie Z.; Bordey, Angélique

    2010-01-01

    Aside from traditional neurotransmission and regulation of secretion, γ-amino butyric acid (GABA) through GABAA receptors negatively regulates proliferation of pluripotent and neural stem cells in embryonic and adult tissue. There has also been evidence that GABAergic signaling and its control over proliferation is not only limited to the nervous system, but is widespread through peripheral organs containing adult stem cells. GABA has emerged as a tumor signaling molecule in the periphery that controls the proliferation of tumor cells and perhaps tumor stem cells. Here, we will discuss GABA's presence as a near-universal signal that may be altered in tumor cells resulting in modified mitotic activity. PMID:19509127

  7. Neural Network Based Modeling and Analysis of LP Control Surface Allocation

    NASA Technical Reports Server (NTRS)

    Langari, Reza; Krishnakumar, Kalmanje; Gundy-Burlet, Karen

    2003-01-01

    This paper presents an approach to interpretive modeling of LP based control allocation in intelligent flight control. The emphasis is placed on a nonlinear interpretation of the LP allocation process as a static map to support analytical study of the resulting closed loop system, albeit in approximate form. The approach makes use of a bi-layer neural network to capture the essential functioning of the LP allocation process. It is further shown via Lyapunov based analysis that under certain relatively mild conditions the resulting closed loop system is stable. Some preliminary conclusions from a study at Ames are stated and directions for further research are given at the conclusion of the paper.

  8. Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

    PubMed Central

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Friehs, Gerhard M.; Black, Michael J.

    2012-01-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain–computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (<3% in one participant). This study suggests that signals from a small ensemble of motor cortical neurons (~40) can be used for natural point-and-click 2-D cursor control of a personal computer. PMID:21278024

  9. Autonomous self-configuration of artificial neural networks for data classification or system control

    NASA Astrophysics Data System (ADS)

    Fink, Wolfgang

    2009-05-01

    Artificial neural networks (ANNs) are powerful methods for the classification of multi-dimensional data as well as for the control of dynamic systems. In general terms, ANNs consist of neurons that are, e.g., arranged in layers and interconnected by real-valued or binary neural couplings or weights. ANNs try mimicking the processing taking place in biological brains. The classification and generalization capabilities of ANNs are given by the interconnection architecture and the coupling strengths. To perform a certain classification or control task with a particular ANN architecture (i.e., number of neurons, number of layers, etc.), the inter-neuron couplings and their accordant coupling strengths must be determined (1) either by a priori design (i.e., manually) or (2) using training algorithms such as error back-propagation. The more complex the classification or control task, the less obvious it is how to determine an a priori design of an ANN, and, as a consequence, the architecture choice becomes somewhat arbitrary. Furthermore, rather than being able to determine for a given architecture directly the corresponding coupling strengths necessary to perform the classification or control task, these have to be obtained/learned through training of the ANN on test data. We report on the use of a Stochastic Optimization Framework (SOF; Fink, SPIE 2008) for the autonomous self-configuration of Artificial Neural Networks (i.e., the determination of number of hidden layers, number of neurons per hidden layer, interconnections between neurons, and respective coupling strengths) for performing classification or control tasks. This may provide an approach towards cognizant and self-adapting computing architectures and systems.

  10. Affective state and locus of control modulate the neural response to threat.

    PubMed

    Harnett, Nathaniel G; Wheelock, Muriah D; Wood, Kimberly H; Ladnier, Jordan C; Mrug, Sylvie; Knight, David C

    2015-11-01

    The ability to regulate the emotional response to threat is critical to healthy emotional function. However, the response to threat varies considerably from person-to-person. This variability may be partially explained by differences in emotional processes, such as locus of control and affective state, which vary across individuals. Although the basic neural circuitry that mediates the response to threat has been described, the impact individual differences in affective state and locus of control have on that response is not well characterized. Understanding how these factors influence the neural response to threat would provide new insight into processes that mediate emotional function. Therefore, the present study used a Pavlovian conditioning procedure to investigate the influence individual differences in locus of control, positive affect, and negative affect have on the brain and behavioral responses to predictable and unpredictable threats. Thirty-two healthy volunteers participated in a fear conditioning study in which predictable and unpredictable threats (i.e., unconditioned stimulus) were presented during functional magnetic resonance imaging (fMRI). Locus of control showed a linear relationship with learning-related ventromedial prefrontal cortex (PFC) activity such that the more external an individual's locus of control, the greater their differential response to predictable versus unpredictable threat. In addition, positive and negative affectivity showed a curvilinear relationship with dorsolateral PFC, dorsomedial PFC, and insula activity, such that those with high or low affectivity showed reduced regional activity compared to those with an intermediate level of affectivity. Further, activity within the PFC, as well as other regions including the amygdala, were linked with the peripheral emotional response as indexed by skin conductance and electromyography. The current findings demonstrate that the neural response to threat within brain regions

  11. Learning techniques to train neural networks as a state selector for inverter-fed induction machines using direct torque control

    SciTech Connect

    Cabrera, L.A.; Elbuluk, M.E.; Zinger, D.S.

    1997-09-01

    Neural networks are receiving attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. This paper discusses the application of neural networks to control induction machines using direct torque control (DTC). A neural network is used to emulate the state selector of the DTC. The training algorithms used in this paper are the backpropagation, adaptive neuron model, extended Kalman filter, and the parallel recursive prediction error. Computer simulations of the motor and neural-network system using the four approaches are presented and compared. Discussions about the parallel recursive prediction error and the extended Kalman filter algorithms as the most promising training techniques is presented, giving their advantages and disadvantages.

  12. A Switching Approach to Designing Finite-Time Synchronization Controllers of Coupled Neural Networks.

    PubMed

    Liu, Xiaoyang; Su, Housheng; Chen, Michael Z Q

    2016-02-01

    This paper is concerned with the finite-time synchronization issue of nonlinear coupled neural networks by designing a new switching pinning controller. For the fixed network topology and control strength, the newly designed controller could optimize the synchronization time by regulating a parameter α (0 ≤ α < 1). The control law presented in this paper covers both continuous controllers and discontinuous ones, which were studied separately in the past. Some criteria are discussed in detail on how to shorten the synchronization time for the strongly connected networks. Finally, the results are generalized to any network topologies containing a directed spanning tree, and one numerical example is given to demonstrate the effectiveness of the theoretical results. PMID:26186796

  13. Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

    NASA Astrophysics Data System (ADS)

    Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

    2013-09-01

    In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

  14. Neural network-based distributed attitude coordination control for spacecraft formation flying with input saturation.

    PubMed

    Zou, An-Min; Kumar, Krishna Dev

    2012-07-01

    This brief considers the attitude coordination control problem for spacecraft formation flying when only a subset of the group members has access to the common reference attitude. A quaternion-based distributed attitude coordination control scheme is proposed with consideration of the input saturation and with the aid of the sliding-mode observer, separation principle theorem, Chebyshev neural networks, smooth projection algorithm, and robust control technique. Using graph theory and a Lyapunov-based approach, it is shown that the distributed controller can guarantee the attitude of all spacecraft to converge to a common time-varying reference attitude when the reference attitude is available only to a portion of the group of spacecraft. Numerical simulations are presented to demonstrate the performance of the proposed distributed controller. PMID:24807141

  15. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    PubMed

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs. PMID:26830003

  16. A neural network controller for hydronic heating systems of solar buildings.

    PubMed

    Argiriou, Athanassios A; Bellas-Velidis, Ioannis; Kummert, Michaël; André, Philippe

    2004-04-01

    An artificial neural network (ANN)-based controller for hydronic heating plants of buildings is presented. The controller has forecasting capabilities: it includes a meteorological module, forecasting the ambient temperature and solar irradiance, an indoor temperature predictor module, a supply temperature predictor module and an optimizing module for the water supply temperature. All ANN modules are based on the Feed Forward Back Propagation (FFBP) model. The operation of the controller has been tested experimentally, on a real-scale office building during real operating conditions. The operation results were compared to those of a conventional controller. The performance was also assessed via numerical simulation. The detailed thermal simulation tool for solar systems and buildings TRNSYS was used. Both experimental and numerical results showed that the expected percentage of energy savings with respect to a conventional controller is of about 15% under North European weather conditions. PMID:15037359

  17. UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model.

    PubMed

    Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Li, Huiyan

    2015-02-01

    A novel closed loop control framework is proposed to inhibit epileptiform wave in a neural mass model by external electric field, where the unscented Kalman filter method is used to reconstruct dynamics and estimate unmeasurable parameters of the model. Specifically speaking, the iterative learning control algorithm is introduced into the framework to optimize the control signal. In the proposed method, the control effect can be significantly improved based on the observation of the past attempts. Accordingly, the proposed method can effectively suppress the epileptiform wave as well as showing robustness to noises and uncertainties. Lastly, the simulation is carried out to illustrate the feasibility of the proposed method. Besides, this work shows potential value to design model-based feedback controllers for epilepsy treatment. PMID:26052360

  18. Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems.

    PubMed

    Han, Seong-Ik; Lee, Jang-Myung

    2014-01-01

    This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator. PMID:24055100

  19. Investigation of neural-net based control strategies for improved power system dynamic performance

    SciTech Connect

    Sobajic, D.J.

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net base system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  20. Crew exploration vehicle (CEV) attitude control using a neural-immunology/memory network

    NASA Astrophysics Data System (ADS)

    Weng, Liguo; Xia, Min; Wang, Wei; Liu, Qingshan

    2015-01-01

    This paper addresses the problem of the crew exploration vehicle (CEV) attitude control. CEVs are NASA's next-generation human spaceflight vehicles, and they use reaction control system (RCS) jet engines for attitude adjustment, which calls for control algorithms for firing the small propulsion engines mounted on vehicles. In this work, the resultant CEV dynamics combines both actuation and attitude dynamics. Therefore, it is highly nonlinear and even coupled with significant uncertainties. To cope with this situation, a neural-immunology/memory network is proposed. It is inspired by the human memory and immune systems. The control network does not rely on precise system dynamics information. Furthermore, the overall control scheme has a simple structure and demands much less computation as compared with most existing methods, making it attractive for real-time implementation. The effectiveness of this approach is also verified via simulation.

  1. Divergent neural substrates of inhibitory control in binge eating disorder relative to other manifestations of obesity

    PubMed Central

    Balodis, Iris M.; Molina, Nathan D.; Kober, Hedy; Worhunsky, Patrick D.; White, Marney A.; Sinha, Rajita; Grilo, Carlos M.; Potenza, Marc N.

    2012-01-01

    An important endeavor involves increasing our understanding of biobehavioral processes underlying different types of obesity. The current study investigated the neural correlates of cognitive control (involving conflict monitoring and response inhibition) in obese individuals with binge eating disorder (BED) as compared to BMI-matched non-BED obese (OB) individuals and lean comparison (LC) participants. Alterations in cognitive control may contribute to differences in behavioral control over eating behaviors in BED and obesity. Participants underwent functional magnetic resonance imaging (fMRI) while completing the Stroop color-word interference task. Relative to the OB and LC groups, activity in the BED group was differentiated by relative hypoactivity in brain areas involved in self-regulation and impulse control. Specifically, the BED group showed diminished activity in the ventromedial prefrontal cortex (vmPFC), inferior frontal gyrus (IFG) and insula during Stroop performance. In addition, dietary restraint scores were negatively correlated with right IFG and vmPFC activation in the BED group, but not in the OB or HC groups. Thus, BED individuals’ diminished ability to recruit impulse-control-related brain regions appears associated with impaired dietary restraint. The observed differences in neural correlates of inhibitory processing in BED relative to OB and LC groups suggest distinct neurobiological contributions to binge eating as a subgroup of obese individuals. PMID:23404820

  2. Giant Panda (Ailuropoda melanoleuca) Buccal Mucosa Tissue as a Source of Multipotent Progenitor Cells

    PubMed Central

    Prescott, Hilary M. A.; Manning, Craig; Gardner, Aaron; Ritchie, William A.; Pizzi, Romain; Girling, Simon; Valentine, Iain; Wang, Chengdong; Jahoda, Colin A. B.

    2015-01-01

    Since the first mammal was cloned, the idea of using this technique to help endangered species has aroused considerable interest. However, several issues limit this possibility, including the relatively low success rate at every stage of the cloning process, and the dearth of usable tissues from these rare animals. iPS cells have been produced from cells from a number of rare mammalian species and this is the method of choice for strategies to improve cloning efficiency and create new gametes by directed differentiation. Nevertheless information about other stem cell/progenitor capabilities of cells from endangered species could prove important for future conservation approaches and adds to the knowledge base about cellular material that can be extremely limited. Multipotent progenitor cells, termed skin-derived precursor (SKP) cells, can be isolated directly from mammalian skin dermis, and human cheek tissue has also been shown to be a good source of SKP-like cells. Recently we showed that structures identical to SKPs termed m-SKPs could be obtained from monolayer/ two dimensional (2D) skin fibroblast cultures. Here we aimed to isolate m-SKPs from cultured cells of three endangered species; giant panda (Ailuropoda melanoleuca); red panda (Ailurus fulgens); and Asiatic lion (Panthera leo persica). m-SKP-like spheres were formed from the giant panda buccal mucosa fibroblasts; whereas dermal fibroblast (DF) cells cultured from abdominal skin of the other two species were unable to generate spheres. Under specific differentiation culture conditions giant panda spheres expressed neural, Schwann, adipogenic and osteogenic cell markers. Furthermore, these buccal mucosa derived spheres were shown to maintain expression of SKP markers: nestin, versican, fibronectin, and P75 and switch on expression of the stem cell marker ABCG2. These results demonstrate that giant panda cheek skin can be a useful source of m-SKP multipotent progenitors. At present lack of sample numbers

  3. Giant Panda (Ailuropoda melanoleuca) Buccal Mucosa Tissue as a Source of Multipotent Progenitor Cells.

    PubMed

    Prescott, Hilary M A; Manning, Craig; Gardner, Aaron; Ritchie, William A; Pizzi, Romain; Girling, Simon; Valentine, Iain; Wang, Chengdong; Jahoda, Colin A B

    2015-01-01

    Since the first mammal was cloned, the idea of using this technique to help endangered species has aroused considerable interest. However, several issues limit this possibility, including the relatively low success rate at every stage of the cloning process, and the dearth of usable tissues from these rare animals. iPS cells have been produced from cells from a number of rare mammalian species and this is the method of choice for strategies to improve cloning efficiency and create new gametes by directed differentiation. Nevertheless information about other stem cell/progenitor capabilities of cells from endangered species could prove important for future conservation approaches and adds to the knowledge base about cellular material that can be extremely limited. Multipotent progenitor cells, termed skin-derived precursor (SKP) cells, can be isolated directly from mammalian skin dermis, and human cheek tissue has also been shown to be a good source of SKP-like cells. Recently we showed that structures identical to SKPs termed m-SKPs could be obtained from monolayer/ two dimensional (2D) skin fibroblast cultures. Here we aimed to isolate m-SKPs from cultured cells of three endangered species; giant panda (Ailuropoda melanoleuca); red panda (Ailurus fulgens); and Asiatic lion (Panthera leo persica). m-SKP-like spheres were formed from the giant panda buccal mucosa fibroblasts; whereas dermal fibroblast (DF) cells cultured from abdominal skin of the other two species were unable to generate spheres. Under specific differentiation culture conditions giant panda spheres expressed neural, Schwann, adipogenic and osteogenic cell markers. Furthermore, these buccal mucosa derived spheres were shown to maintain expression of SKP markers: nestin, versican, fibronectin, and P75 and switch on expression of the stem cell marker ABCG2. These results demonstrate that giant panda cheek skin can be a useful source of m-SKP multipotent progenitors. At present lack of sample numbers

  4. Cognitive-affective neural plasticity following active-controlled mindfulness intervention

    PubMed Central

    Allen, Micah; Dietz, Martin; Blair, Karina S.; van Beek, Martijn; Rees, Geraint; Vestergaard-Poulsen, Peter; Lutz, Antoine; Roepstorff, Andreas

    2015-01-01

    Mindfulness meditation is a set of attention-based, regulatory and self-inquiry training regimes. Although the impact of mindfulness meditation training (MT) on self-regulation is well established, the neural mechanisms supporting such plasticity are poorly understood. MT is thought to act on attention through interoceptive salience and attentional control mechanisms, but until now conflicting evidence from behavioral and neural measures has made it difficult to distinguish the role of these mechanisms. To resolve this question we conducted a fully randomized 6-week longitudinal trial of MT, explicitly controlling for cognitive and treatment effects with an active control group. We measured behavioral metacognition and whole-brain Blood Oxygenation Level Dependent (BOLD) signals using functional MRI during an affective Stroop task before and after intervention. Although both groups improved significantly on a response-inhibition task, only the MT group showed reduced affective Stroop conflict. Moreover, the MT group displayed greater dorsolateral prefrontal cortex (DLPFC) responses during executive processing, consistent with increased recruitment of top-down mechanisms to resolve conflict. In contrast, we did not observe overall group by time interactions on negative affect-related RTs or BOLD responses. However, only participants with the greatest amount of MT practice showed improvements in response-inhibition and increased recruitment of dorsal anterior cingulate cortex (dACC), medial prefrontal cortex (mPFC), and right anterior insula during negative valence processing. Collectively our findings highlight the importance of active control in MT research, and indicate unique neural mechanisms for progressive stages of mindfulness training. PMID:23115195

  5. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    PubMed

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. PMID:22738782

  6. Design of neural networks for fast convergence and accuracy: dynamics and control.

    PubMed

    Maghami, P G; Sparks, D R

    2000-01-01

    A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach. PMID:18249744

  7. Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

    NASA Technical Reports Server (NTRS)

    Maghami, Peiman G.; Sparks, Dean W., Jr.

    1997-01-01

    A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

  8. Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

    NASA Technical Reports Server (NTRS)

    Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

    1991-01-01

    Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

  9. Neural net-based controller for flutter suppression using ASTROS with smart structures

    NASA Astrophysics Data System (ADS)

    Nam, Changho; Chen, P. C.; Liu, Danny D.; Chattopadhyay, Aditi; Kim, Jongsun

    2000-06-01

    Recent development of a smart structures module and its successful integration with a multidisciplinary design optimization software ASTROS* and an Aeroservoelasticity (ASE) module is presented. A modeled F-16 wing using piezoelectric (PZT) actuators was used as an example to demonstrate the integrated software capability to design a flutter suppression system. For an active control design, neural network based robust controller will be used for this study. A smart structures module is developed by modifying the existing thermal loads module in ASTROS* in order to include the effects of the induced strain due to piezoelectric (PZT) actuation. The thermal-PZT equivalence model enables the modifications of the thermal stress module to accommodate the smart structures module in ASTROS*. ZONA developed the control surface (CS)/PZT equivalence model principle, which ensures the interchangeability between the CS force input and the PZT force input to the ASE modules in ASTROS*. The results show that the neural net based controller can increase the flutter speed.

  10. No Evidence That Gratitude Enhances Neural Performance Monitoring or Conflict-Driven Control

    PubMed Central

    Saunders, Blair; He, Frank F. H.; Inzlicht, Michael

    2015-01-01

    It has recently been suggested that gratitude can benefit self-regulation by reducing impulsivity during economic decision making. We tested if comparable benefits of gratitude are observed for neural performance monitoring and conflict-driven self-control. In a pre-post design, 61 participants were randomly assigned to either a gratitude or happiness condition, and then performed a pre-induction flanker task. Subsequently, participants recalled an autobiographical event where they had felt grateful or happy, followed by a post-induction flanker task. Despite closely following existing protocols, participants in the gratitude condition did not report elevated gratefulness compared to the happy group. In regard to self-control, we found no association between gratitude—operationalized by experimental condition or as a continuous predictor—and any control metric, including flanker interference, post-error adjustments, or neural monitoring (the error-related negativity, ERN). Thus, while gratitude might increase economic patience, such benefits may not generalize to conflict-driven control processes. PMID:26633830

  11. Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

    SciTech Connect

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

    Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

  12. Feasibility of EMG-based neural network controller for an upper extremity neuroprosthesis.

    PubMed

    Hincapie, Juan Gabriel; Kirsch, Robert F

    2009-02-01

    The overarching goal of this project is to provide shoulder and elbow function to individuals with C5/C6 spinal cord injury (SCI) using functional electrical stimulation (FES), increasing the functional outcomes currently provided by a hand neuroprosthesis. The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient to predict appropriate stimulation levels for several paralyzed muscles in the upper extremity. The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities. Several arm movements were recorded from able-bodied subjects and these kinematics served as the inputs to inverse dynamic simulations that predicted muscle activation patterns corresponding to the movements recorded. A system identification procedure was used to identify an optimal reduced set of voluntary input muscles from the larger set that are typically under voluntary control in C5 SCI. These voluntary activations were used as the inputs to the ANN and muscles that are typically paralyzed in C5 SCI were the outputs to be predicted. The neural network controller was able to predict the needed FES paralyzed muscle activations from "voluntary" activations with less than a 3.6% RMS prediction error. PMID:19211327

  13. Feasibility of EMG-Based Neural Network Controller for an Upper Extremity Neuroprosthesis

    PubMed Central

    Hincapie, Juan Gabriel; Kirsch, Robert F.

    2013-01-01

    The overarching goal of this project is to provide shoulder and elbow function to individuals with C5/C6 Spinal Cord Injury (SCI) using functional electrical stimulation (FES), increasing the functional outcomes currently provided by a hand neuroprosthesis. The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient to predict appropriate stimulation levels for several paralyzed muscles in the upper extremity. The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities. Several arm movements were recorded from able-bodied subjects and these kinematics served as the inputs to inverse dynamic simulations that predicted muscle activation patterns corresponding to the movements recorded. A system identification procedure was used to identify an optimal reduced set of voluntary input muscles from the larger set that are typically under voluntary control in C5 SCI. These voluntary activations were used as the inputs to the ANN and muscles that are typically paralyzed in C5 SCI were the outputs to be predicted. The neural network controller was able to predict the needed FES paralyzed muscle activations from “voluntary” activations with less than a 3.6% RMS prediction error. PMID:19211327

  14. Strategy of arm movement control is determined by minimization of neural effort for joint coordination.

    PubMed

    Dounskaia, Natalia; Shimansky, Yury

    2016-06-01

    Optimality criteria underlying organization of arm movements are often validated by testing their ability to adequately predict hand trajectories. However, kinematic redundancy of the arm allows production of the same hand trajectory through different joint coordination patterns. We therefore consider movement optimality at the level of joint coordination patterns. A review of studies of multi-joint movement control suggests that a 'trailing' pattern of joint control is consistently observed during which a single ('leading') joint is rotated actively and interaction torque produced by this joint is the primary contributor to the motion of the other ('trailing') joints. A tendency to use the trailing pattern whenever the kinematic redundancy is sufficient and increased utilization of this pattern during skillful movements suggests optimality of the trailing pattern. The goal of this study is to determine the cost function minimization of which predicts the trailing pattern. We show that extensive experimental testing of many known cost functions cannot successfully explain optimality of the trailing pattern. We therefore propose a novel cost function that represents neural effort for joint coordination. That effort is quantified as the cost of neural information processing required for joint coordination. We show that a tendency to reduce this 'neurocomputational' cost predicts the trailing pattern and that the theoretically developed predictions fully agree with the experimental findings on control of multi-joint movements. Implications for future research of the suggested interpretation of the trailing joint control pattern and the theory of joint coordination underlying it are discussed. PMID:26983620

  15. Neural Computation Scheme of Compound Control: Tacit Learning for Bipedal Locomotion

    NASA Astrophysics Data System (ADS)

    Shimoda, Shingo; Kimura, Hidenori

    The growing need for controlling complex behaviors of versatile robots working in unpredictable environment has revealed the fundamental limitation of model-based control strategy that requires precise models of robots and environments before their operations. This difficulty is fundamental and has the same root with the well-known frame problem in artificial intelligence. It has been a central long standing issue in advanced robotics, as well as machine intelligence, to find a prospective clue to attack this fundamental difficulty. The general consensus shared by many leading researchers in the related field is that the body plays an important role in acquiring intelligence that can conquer unknowns. In particular, purposeful behaviors emerge during body-environment interactions with the help of an appropriately organized neural computational scheme that can exploit what the environment can afford. Along this line, we propose a new scheme of neural computation based on compound control which represents a typical feature of biological controls. This scheme is based on classical neuron models with local rules that can create macroscopic purposeful behaviors. This scheme is applied to a bipedal robot and generates the rhythm of walking without any model of robot dynamics and environments.

  16. Application of an Artificial Neural Tissue Controller to Multirobot Lunar ISRU Operations

    NASA Astrophysics Data System (ADS)

    Thangavelautham, Jekanthan; Smith, Alexander; Boucher, Dale; Richard, Jim; D'Eleuterio, Gabriele M. T.

    2007-01-01

    Automation of mining and resource utilization processes on the Moon with teams of autonomous robots holds considerable promise for establishing a lunar base. We present an Artificial Neural Tissue (ANT) architecture as a control system for autonomous multirobot tasks. An Artificial Neural Tissue (ANT) approach requires much less human supervision and pre-programmed human expertise than previous techniques. Only a single global fitness function and a set of allowable basis behaviors need be specified. An evolutionary (Darwinian) selection process is used to train controllers for the task at hand in simulation and is verified on hardware. This process results in the emergence of novel functionality through the task decomposition of mission goals. ANT based controllers are shown to exhibit self-organization, employ stigmergy (communication mediated through the environment) and make use of templates (unlabeled environmental cues). With lunar in-situ resource utilization (ISRU) efforts in mind, ANT controllers have been tested on a multirobot resource gathering task in which teams of robots with no explicit supervision can successfully avoid obstacles, explore terrain, locate resource material and collect it in a designated area by using a light beacon for reference and interpreting unlabeled perimeter markings.

  17. Adaptive Neural Control for a Class of Pure-Feedback Nonlinear Systems via Dynamic Surface Technique.

    PubMed

    Liu, Zongcheng; Dong, Xinmin; Xue, Jianping; Li, Hongbo; Chen, Yong

    2016-09-01

    This brief addresses the adaptive control problem for a class of pure-feedback systems with nonaffine functions possibly being nondifferentiable. Without using the mean value theorem, the difficulty of the control design for pure-feedback systems is overcome by modeling the nonaffine functions appropriately. With the help of neural network approximators, an adaptive neural controller is developed by combining the dynamic surface control (DSC) and minimal learning parameter (MLP) techniques. The key features of our approach are that, first, the restrictive assumptions on the partial derivative of nonaffine functions are removed, second, the DSC technique is used to avoid "the explosion of complexity" in the backstepping design, and the number of adaptive parameters is reduced significantly using the MLP technique, third, smooth robust compensators are employed to circumvent the influences of approximation errors and disturbances. Furthermore, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, the simulation results are provided to demonstrate the effectiveness of the designed method. PMID:26277010

  18. Locomotion Control of MEMS Micro Robot Using Pulse-Type Hardware Neural Networks

    NASA Astrophysics Data System (ADS)

    Saito, Ken; Okazaki, Kazuto; Ogiwara, Tatsuya; Takato, Minami; Saeki, Katsutoshi; Sekine, Yoshifumi; Uchikoba, Fumio

    This paper presents the locomotion control of micro electro mechanical systems (MEMS) micro robot. MEMS micro robot demonstrates the locomotion control by the pulse-type hardware neural networks (P-HNN). P-HNN generates oscillatory patterns of electrical activity such as living organisms. The basic component of P-HNN is pulse-type hardware neuron model (P-HNM). P-HNM has same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics and enables the generation of continuous action potentials. P-HNN was constructed by MOSFETs, can be integrated by CMOS technology. Same as the living organisms P-HNN realized the robot control without using software programs, or A/D converters. The size of micro robot fabricated by the MEMS technology was 4×4×3.5 [mm]. The frame of robot was made of silicon wafer, equipped with rotary type actuators, link mechanisms and 6 legs. MEMS micro robot emulated the locomotion method and the neural networks of the insect by rotary actuators, link mechanisms and P-HNN. As a result, we show that P-HNN can control the forward and backward locomotion of fabricated MEMS micro robot, and also switched the direction by inputting the external trigger pulse. The locomotion speed was 19.5 [mm/min] and the step width was 1.3 [mm].

  19. Predictive ion source control using artificial neural network for RFT-30 cyclotron

    NASA Astrophysics Data System (ADS)

    Kong, Young Bae; Hur, Min Goo; Lee, Eun Je; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-01-01

    An RFT-30 cyclotron is a 30 MeV proton accelerator for radioisotope production and fundamental research. The ion source of the RFT-30 cyclotron creates plasma from hydrogen gas and transports an ion beam into the center region of the cyclotron. Ion source control is used to search source parameters for best quality of the ion beam. Ion source control in a real system is a difficult and time consuming task, and the operator should search the source parameters by manipulating the cyclotron directly. In this paper, we propose an artificial neural network based predictive control approach for the RFT-30 ion source. The proposed approach constructs the ion source model by using an artificial neural network and finds the optimized parameters with the simulated annealing algorithm. To analyze the performance of the proposed approach, we evaluated the simulations with the experimental data of the ion source. The performance results show that the proposed approach can provide an efficient way to analyze and control the ion source of the RFT-30 cyclotron.

  20. Frontal preparatory neural oscillations associated with cognitive control: A developmental study comparing young adults and adolescents.

    PubMed

    Hwang, Kai; Ghuman, Avniel S; Manoach, Dara S; Jones, Stephanie R; Luna, Beatriz

    2016-08-01

    Functional magnetic resonance imaging (fMRI) studies suggest that age-related changes in the frontal cortex may underlie developmental improvements in cognitive control. In the present study we used magnetoencephalography (MEG) to identify frontal oscillatory neurodynamics that support age-related improvements in cognitive control during adolescence. We characterized the differences in neural oscillations in adolescents and adults during the preparation to suppress a prepotent saccade (antisaccade trials-AS) compared to preparing to generate a more automatic saccade (prosaccade trials-PS). We found that for adults, AS were associated with increased beta-band (16-38Hz) power in the dorsal lateral prefrontal cortex (DLPFC), enhanced alpha- to low beta-band (10-18Hz) power in the frontal eye field (FEF) that predicted performance, and increased cross-frequency alpha-beta (10-26Hz) amplitude coupling between the DLPFC and the FEF. Developmental comparisons between adults and adolescents revealed similar engagement of DLPFC beta-band power but weaker FEF alpha-band power, and lower cross-frequency coupling between the DLPFC and the FEF in adolescents. These results suggest that lateral prefrontal neural activity associated with cognitive control is adult-like by adolescence; the development of cognitive control from adolescence to adulthood is instead associated with increases in frontal connectivity and strengthening of inhibition signaling for suppressing task-incompatible processes. PMID:27173759

  1. Dopamine controls the neural dynamics of memory signals and retrieval accuracy.

    PubMed

    Apitz, Thore; Bunzeck, Nico

    2013-11-01

    The human brain is capable of differentiating between new and already stored information rapidly to allow optimal behavior and decision-making. Although the neural mechanisms of novelty discrimination were often described as temporally constant (ie, with specific latencies), recent electrophysiological studies have demonstrated that the onset of neural novelty signals (ie, differences in event-related responses to new and old items) can be accelerated by reward motivation. While the precise physiological mechanisms underlying this acceleration remain unclear, the involvement of the neurotransmitter dopamine in both novelty and reward processing suggests that enhanced dopamine levels in the context of reward prospect may have a role. To investigate this hypothesis, we used magnetoencephalography (MEG) in combination with an old/new recognition memory task in which correct discrimination between old and new items was rewarded. Importantly, before the task, human subjects received either 150 mg of the dopamine precursor levodopa or placebo. For the placebo group, old/new signals peaked at ∼100 ms after stimulus onset over left temporal/occipital sensors. In contrast, after levodopa administration earliest old/new effects only emerged after ∼400 ms and retrieval accuracy was reduced as expressed in lower d' values. As such, our results point towards a previously unreported role of dopamine in controlling the chronometry of neural processes underlying the distinction between old and new information. They also suggest that this relationship follows a nonlinear function whereby slightly enhanced dopamine levels accelerate neural/cognitive processes and excessive dopamine levels impair them. PMID:23728140

  2. H∞ control problem for Hopfield neural networks with interval time-varying delay

    NASA Astrophysics Data System (ADS)

    Emharuethai, Chanikan

    2016-02-01

    In this paper, we consider H∞ control problem for a class Hopfield neural networks with interval time-varying delay. The time delay is a continuous function belonging to a given interval, but not necessariry differentiable. The stabilizing controllers to be designed must satisfy some exponential stability constraints on the closed-loop poles. Based on the construction of improved Lyapunov-Krasovskii functionals combined with Newton-Leibniz formula. H∞ controller is designed via memoryless state feedback control and new sufficient conditions for the existence of the H∞ state-feedback for the system are given in terms of linear matrix inequalities (LMIs). Numerical examples are given to illustrate the effectiveness of the obtained result.

  3. A neural based intelligent flight control system for the NASA F-15 flight research aircraft

    NASA Technical Reports Server (NTRS)

    Urnes, James M.; Hoy, Stephen E.; Ladage, Robert N.; Stewart, James

    1993-01-01

    A flight control concept that can identify aircraft stability properties and continually optimize the aircraft flying qualities has been developed by McDonnell Aircraft Company under a contract with the NASA-Dryden Flight Research Facility. This flight concept, termed the Intelligent Flight Control System, utilizes Neural Network technology to identify the host aircraft stability and control properties during flight, and use this information to design on-line the control system feedback gains to provide continuous optimum flight response. This self-repairing capability can provide high performance flight maneuvering response throughout large flight envelopes, such as needed for the National Aerospace Plane. Moreover, achieving this response early in the vehicle's development schedule will save cost.

  4. Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

    PubMed

    Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

    2014-03-15

    The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. PMID:24333753

  5. Inactivation of Geminin in neural crest cells affects the generation and maintenance of enteric progenitor cells, leading to enteric aganglionosis.

    PubMed

    Stathopoulou, Athanasia; Natarajan, Dipa; Nikolopoulou, Pinelopi; Patmanidi, Alexandra L; Lygerou, Zoi; Pachnis, Vassilis; Taraviras, Stavros

    2016-01-15

    Neural crest cells comprise a multipotent, migratory cell population that generates a diverse array of cell and tissue types, during vertebrate development. Enteric Nervous System controls the function of the gastrointestinal tract and is mainly derived from the vagal and sacral neural crest cells. Deregulation on self-renewal and differentiation of the enteric neural crest cells is evident in enteric nervous system disorders, such as Hirschsprung disease, characterized by the absence of ganglia in a variable length of the distal bowel. Here we show that Geminin is essential for Enteric Nervous System generation as mice that lacked Geminin expression specifically in neural crest cells revealed decreased generation of vagal neural crest cells, and enteric neural crest cells (ENCCs). Geminin-deficient ENCCs showed increased apoptosis and decreased cell proliferation during the early stages of gut colonization. Furthermore, decreased number of committed ENCCs in vivo and the decreased self-renewal capacity of enteric progenitor cells in vitro, resulted in almost total aganglionosis resembling a severe case of Hirschsprung disease. Our results suggest that Geminin is an important regulator of self-renewal and survival of enteric nervous system progenitor cells. PMID:26658318

  6. Neural correlates of impaired cognitive-control over working memory in Schizophrenia

    PubMed Central

    Eich, Teal S.; Nee, Derek Evan; Insel, Catherine; Malapani, Chara; Smith, Edward E.

    2014-01-01

    Background One of the most common deficits in patients with schizophrenia (SZ) is in working memory (WM), which has wide-reaching impacts across cognition. However, prior approaches to studying WM in SZ have employed tasks that require multiple cognitive-control processes, making it difficult to determine which specific cognitive and neural processes underlie the WM impairment. Methods We used fMRI to investigate component processes of WM in SZ. Eighteen healthy controls (HCs) and 18 patients with SZ performed an item-recognition task that permitted separate neural assessments of 1) WM maintenance, 2) inhibition, and 3) interference-control in response to recognition probes. Results Prior to inhibitory demands, posterior ventrolateral prefrontal cortex (VLPFC), an area involved in WM maintenance, was activated to a similar degree in both HCs and patients, indicating preserved maintenance operations in SZ. When cued to inhibit items from WM, HCs showed reduced activation in posterior VLPFC, commensurate with appropriately inhibiting items from WM. However, these inhibition-related reductions were absent in patients. When later probed with items that should have been inhibited, patients showed reduced behavioral performance and increased activation in mid-VLPFC, an area implicated in interference-control. A mediation analysis indicated that impaired inhibition led to increased reliance on interference-control and reduced behavioral performance. Conclusions In SZ, impaired control over memory, manifested through proactive inhibitory deficits, leads to increased reliance on reactive interference-control processes. The strain on interference-control processes results in reduced behavioral performance. Thus, inhibitory deficits in SZ may underlie widespread impairments in WM and cognition. PMID:24239131

  7. Neuromechanism study of insect-machine interface: flight control by neural electrical stimulation.

    PubMed

    Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

    2014-01-01

    The insect-machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee-machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control. PMID:25409523

  8. Parametric motion control of robotic arms: A biologically based approach using neural networks

    NASA Technical Reports Server (NTRS)

    Bock, O.; D'Eleuterio, G. M. T.; Lipitkas, J.; Grodski, J. J.

    1993-01-01

    A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

  9. Neuromechanism Study of Insect–Machine Interface: Flight Control by Neural Electrical Stimulation

    PubMed Central

    Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A.; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

    2014-01-01

    The insect–machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee–machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control. PMID:25409523

  10. The design and optimization for light-algae bioreactor controller based on Artificial Neural Network-Model Predictive Control

    NASA Astrophysics Data System (ADS)

    Hu, Dawei; Liu, Hong; Yang, Chenliang; Hu, Enzhu

    As a subsystem of the bioregenerative life support system (BLSS), light-algae bioreactor (LABR) has properties of high reaction rate, efficiently synthesizing microalgal biomass, absorbing CO2 and releasing O2, so it is significant for BLSS to provide food and maintain gas balance. In order to manipulate the LABR properly, it has been designed as a closed-loop control system, and technology of Artificial Neural Network-Model Predictive Control (ANN-MPC) is applied to design the controller for LABR in which green microalgae, Spirulina platensis is cultivated continuously. The conclusion is drawn by computer simulation that ANN-MPC controller can intelligently learn the complicated dynamic performances of LABR, and automatically, robustly and self-adaptively regulate the light intensity illuminating on the LABR, hence make the growth of microalgae in the LABR be changed in line with the references, meanwhile provide appropriate damping to improve markedly the transient response performance of LABR.

  11. Adaptive attitude controller for a satellite based on neural network in the presence of unknown external disturbances and actuator faults

    NASA Astrophysics Data System (ADS)

    Fazlyab, Ali Reza; Fani Saberi, Farhad; Kabganian, Mansour

    2016-01-01

    In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite. The proposed attitude control is based on nonlinear modified Rodrigues parameters feedback control in the presence of unknown terms like external disturbances and actuator faults. In order to eliminate the effect of the uncertainties, a multilayer neural network with a new learning rule will be designed appropriately. In this method, asymptotic stability of the proposed algorithm has been proven in the presence of unknown terms based on Lyapunov stability theorem. Finally, the performance of the designed attitude controller is investigated by simulations.

  12. Erythropoietin guides multipotent hematopoietic progenitor cells toward an erythroid fate

    PubMed Central

    Grover, Amit; Mancini, Elena; Moore, Susan; Mead, Adam J.; Atkinson, Deborah; Rasmussen, Kasper D.; O’Carroll, Donal; Jacobsen, Sten Eirik W.

    2014-01-01

    The erythroid stress cytokine erythropoietin (Epo) supports the development of committed erythroid progenitors, but its ability to act on upstream, multipotent cells remains to be established. We observe that high systemic levels of Epo reprogram the transcriptomes of multi- and bipotent hematopoietic stem/progenitor cells in vivo. This induces erythroid lineage bias at all lineage bifurcations known to exist between hematopoietic stem cells (HSCs) and committed erythroid progenitors, leading to increased erythroid and decreased myeloid HSC output. Epo, therefore, has a lineage instructive role in vivo, through suppression of non-erythroid fate options, demonstrating the ability of a cytokine to systematically bias successive lineage choices in favor of the generation of a specific cell type. PMID:24493804

  13. [Multipotent mesenchymal stromal and immune cells interaction: reciprocal effects].

    PubMed

    Andreeva, E R; Buravkova, L B

    2012-12-01

    Adult multipotent mesenchymal stromal cells (MMSCs) are considered now as one of the key players in physiological and pathological tissue remodeling. Clarification of the mechanisms that mediate MMSC functions, is one of the most intriguing issues in modern cell physiology. Present Review summarizes current understanding of the MMSC effects on different types of immune cells. The realization of MMSC immunomodulatory capacity is considered as a contribution of direct cell-to-cell contacts, soluble mediators and of local microenvironmental factors, the most important of which is the partial pressure of oxygen. MMSCs and immune cells interaction is discussed in the terms of reciprocal effects, modifying properties of all "partner cells". Special attention is paid to the influence of immune cells on the MMSCs. "Immunosuppressive" phenomenon of MMSCs is considered as the integral part of the "response to injury" mechanism. PMID:23461191

  14. Novel neural networks-based fault tolerant control scheme with fault alarm.

    PubMed

    Shen, Qikun; Jiang, Bin; Shi, Peng; Lim, Cheng-Chew

    2014-11-01

    In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques. PMID:25014982

  15. A novel process monitoring and control system based on a neural manufacturing concept

    SciTech Connect

    Fu, Chi Yung; Petrich, L.; Law, B.

    1993-09-14

    This paper describes our work to produce ``smart`` equipment using a neural network to provide intelligence for process monitoring, adaptive control, metrology, and equipment diagnostics. This novel system will improve both quality and yield for critical thin films used in semiconductors, superconductors, high-density magnetic and optical storage, and advanced displays, all of which are critical to maintaining our leadership in the multibillion-dollar electronic and computer industries. The equipment will have on-line, real-time diagnostics capabilities to detect component problems early in order to minimize maintenance and downtime.

  16. Fuzzy-Neural Controller in Service Requests Distribution Broker for SOA-Based Systems

    NASA Astrophysics Data System (ADS)

    Fras, Mariusz; Zatwarnicka, Anna; Zatwarnicki, Krzysztof

    The evolution of software architectures led to the rising importance of the Service Oriented Architecture (SOA) concept. This architecture paradigm support building flexible distributed service systems. In the paper the architecture of service request distribution broker designed for use in SOA-based systems is proposed. The broker is built with idea of fuzzy control. The functional and non-functional request requirements in conjunction with monitoring of execution and communication links are used to distribute requests. Decisions are made with use of fuzzy-neural network.

  17. Neural control of the female urethral and anal rhabdosphincters and pelvic floor muscles

    PubMed Central

    de Groat, William C.

    2010-01-01

    The urethral rhabdosphincter and pelvic floor muscles are important in maintenance of urinary continence and in preventing descent of pelvic organs [i.e., pelvic organ prolapse (POP)]. Despite its clinical importance and complexity, a comprehensive review of neural control of the rhabdosphincter and pelvic floor muscles is lacking. The present review places historical and recent basic science findings on neural control into the context of functional anatomy of the pelvic muscles and their coordination with visceral function and correlates basic science findings with clinical findings when possible. This review briefly describes the striated muscles of the pelvis and then provides details on the peripheral innervation and, in particular, the contributions of the pudendal and levator ani nerves to the function of the various pelvic muscles. The locations and unique phenotypic characteristics of rhabdosphincter motor neurons located in Onuf's nucleus, and levator ani motor neurons located diffusely in the sacral ventral horn, are provided along with the locations and phenotypes of primary afferent neurons that convey sensory information from these muscles. Spinal and supraspinal pathways mediating excitatory and inhibitory inputs to the motor neurons are described; the relative contributions of the nerves to urethral function and their involvement in POP and incontinence are discussed. Finally, a detailed summary of the neurochemical anatomy of Onuf's nucleus and the pharmacological control of the rhabdosphincter are provided. PMID:20484700

  18. Neural control of adrenal medullary and cortical blood flow during hemorrhage

    SciTech Connect

    Breslow, M.J.; Jordan, D.A.; Thellman, S.T.; Traystman, R.J.

    1987-03-01

    Hemorrhagic hypotension produces an increase in adrenal medullary blood flow and a decrease in adrenal cortical blood flow. To determine whether changes in adrenal blood flow during hemorrhage are neurally mediated, the authors compared blood flow responses following adrenal denervation (splanchnic nerve section) with changes in the contralateral, neurally intact adrenal. Carbonized microspheres labeled with /sup 153/Gd, /sup 114/In, /sup 113/Sn, /sup 103/Ru, /sup 95/Nb or /sup 46/Se were used. Blood pressure was reduced and maintained at 60 mmHg for 25 min by hemorrhage into a pressurized bottle system. Adrenal cortical blood flow decreased to 50% of control with hemorrhage in both the intact and denervated adrenal. Adrenal medullary blood flow increased to four times control levels at 15 and 25 min posthemorrhage in the intact adrenal, but was reduced to 50% of control at 3, 5, and 10 min posthemorrhage in the denervated adrenal. In a separate group of dogs, the greater splanchnic nerve on one side was electrically stimulated at 2, 5, or 15 Hz for 40 min. Adrenal medullary blood flow increased 5- to 10-fold in the stimulated adrenal but was unchanged in the contralateral, nonstimulated adrenal. Adrenal cortical blood flow was not affected by nerve stimulation. They conclude that activity of the splanchnic nerve profoundly affects adrenal medullary vessels but not adrenal cortical vessels and mediates the observed increase in adrenal medullary blood flow during hemorrhagic hypotension.

  19. Neural Network-Based Control of Networked Trilateral Teleoperation With Geometrically Unknown Constraints.

    PubMed

    Li, Zhijun; Xia, Yuanqing; Wang, Dehong; Zhai, Di-Hua; Su, Chun-Yi; Zhao, Xingang

    2016-05-01

    Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force-motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach. PMID:25956001

  20. Peaking-Free Output-Feedback Adaptive Neural Control Under a Nonseparation Principle.

    PubMed

    Pan, Yongping; Sun, Tairen; Yu, Haoyong

    2015-12-01

    High-gain observers have been extensively applied to construct output-feedback adaptive neural control (ANC) for a class of feedback linearizable uncertain nonlinear systems under a nonlinear separation principle. Yet due to static-gain and linear properties, high-gain observers are usually subject to peaking responses and noise sensitivity. Existing adaptive neural network (NN) observers cannot effectively relax the limitations of high-gain observers. This paper presents an output-feedback indirect ANC strategy under a nonseparation principle, where a hybrid estimation scheme that integrates an adaptive NN observer with state variable filters is proposed to estimate plant states. By applying a single Lyapunov function candidate to the entire system, it is proved that the closed-loop system achieves practical asymptotic stability under a relatively low observer gain dominated by controller parameters. Our approach can completely avoid peaking responses without control saturation while keeping favourable noise rejection ability. Simulation results have shown effectiveness and superiority of this approach. PMID:25794400