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Sample records for artificial neural nets

  1. Hadamard design and artificial neural nets

    SciTech Connect

    Kuerten, K.E. Universitaet Wien ); Klingen, N. )

    1993-04-01

    Hadamard theory is shown to play an important role in the generation of Boolean decision functions, a fundamental tool in the field of artificial neural network design. Based on a group-theoretic introduction of a complete set of Hadamard vectors, whose matrices are of the order of a power of two, the authors classify subsets according to the degree of their linear dependence. They show in the thermodynamic limit that essentially the whole Hadamard space is occupied by representatives with defect not exceeding two or three. 15 refs., 1 fig.

  2. Applications of artificial neural nets in structural mechanics

    NASA Technical Reports Server (NTRS)

    Berke, Laszlo; Hajela, Prabhat

    1990-01-01

    A brief introduction to the fundamental of Neural Nets is given, followed by two applications in structural optimization. In the first case, the feasibility of simulating with neural nets the many structural analyses performed during optimization iterations was studied. In the second case, the concept of using neural nets to capture design expertise was studied.

  3. Surface daytime net radiation estimation using artificial neural networks

    DOE PAGES

    Jiang, Bo; Zhang, Yi; Liang, Shunlin; ...

    2014-11-11

    Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010more » both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.« less

  4. Surface daytime net radiation estimation using artificial neural networks

    SciTech Connect

    Jiang, Bo; Zhang, Yi; Liang, Shunlin; Zhang, Xiaotong; Xiao, Zhiqiang

    2014-11-11

    Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.

  5. Application of artificial neural nets to Shashlik calorimetry

    NASA Astrophysics Data System (ADS)

    Bonesini, M.; Gumenyuk, S.; Paganoni, M.; Petrovykh, L.; Terranova, F.

    1997-02-01

    Artificial Neural Networks (ANN) are powerful tools widely used in high-energy physics to solve track finding and particle identification problems. An entirely new class of application is related to the problem of recovering the information lost during data taking or signal transmission. Good performances can be reached by ANN when the events are described by quite regular patterns. Such a method was used for the DELPHI luminosity monitor (STIC) to recover calorimeter dead channels. A comparison with more traditional techniques is also given.

  6. Artificial awareness for robots using artificial neural nets to monitor robotic workcells

    SciTech Connect

    Tucker, S.D.; Ray, L.P.

    1997-04-01

    Current robotic systems are unable to recognize most unexpected changes in the work environment, such as tool breakage, workpiece motion, or sensor failure. Unless halted by a human operator, they are likely to continue actions that are at best inappropriate, and at worst may cause damage to the workpiece or robot. This project investigated use of Artificial Neural Networks (ANNs) to learn the expected characteristics of sensor data during normal operations, recognize when data no longer is consistent with normal operation, suspend operations and alert a human operator. Data on force and torque applied at the robot tool tip were collected from two workcells: a robotic deburring system and a robot material-handling system. Data were collected for normal operations and for operations in which a fault condition was introduced. Data simulating sensor failure and excessive sensor noise were generated. Artificial Neural Networks (ANN) were trained to classify operating conditions; several ANN architectures were tested. The selected ANNs were able to correctly classify all valid operating conditions and the majority of fault conditions over the entire range of operating conditions, having {open_quotes}learned{close_quotes} the expected force/torque data. Most faults introduced appreciable error in the data; these were correctly classified. However, a small minority of faults did not give rise to a detectable difference in force and torque data. It is believed that these faults could be detected using other sensors. The computational workload varies with the implementation, but is moderate: up to 2.3 megaflops. This makes implementation of a real-time workcell monitor feasible.

  7. Pattern recognition of vertical strabismus using an artificial neural network (StrabNet).

    PubMed

    Chandna, Arvind; Fisher, Anthony C; Cunningham, Ian; Stone, Deborah; Mitchell, Maureen

    2009-01-01

    Our goal is to develop an accessible expert system (StrabNet) that will assist in the clinical diagnosis of vertical strabismus, form the basis of a teaching/learning tool, and contribute to the audit process. Potentially, this model can be extended to other strabismus deviations. Vertical deviations were separated into eight classifications (diagnoses). An expert system based on architecture of artificial neural networks learned the patterns for each class of deviation based on 10 prism cover-test measurements (9 cardinal positions and near fixation). The expert system was tested with previously unseen and real-patient data. This system was extended to a reduced model requiring only six measurements (primary position, right, left, up, down gaze, and near fixation), and evaluated with real patient data. A freely available Web implementation is available on the Internet at www.StrabNet.com . The expert system was found to be highly accurate at diagnosing vertical strabismus (100% and approximately 96% for ten and six measurement models, respectively) from one of the eight classes. StrabNet is of demonstrable value in diagnosing commonly presenting vertical deviations from prism cover test (PCT) measurements. Its potential role in teaching and in audit is identified.

  8. Comparison of polynomial approximations and artificial neural nets for response surfaces in engineering optimization

    NASA Technical Reports Server (NTRS)

    Carpenter, William C.

    1991-01-01

    Engineering optimization problems involve minimizing some function subject to constraints. In areas such as aircraft optimization, the constraint equations may be from numerous disciplines such as transfer of information between these disciplines and the optimization algorithm. They are also suited to problems which may require numerous re-optimizations such as in multi-objective function optimization or to problems where the design space contains numerous local minima, thus requiring repeated optimizations from different initial designs. Their use has been limited, however, by the fact that development of response surfaces randomly selected or preselected points in the design space. Thus, they have been thought to be inefficient compared to algorithms to the optimum solution. A development has taken place in the last several years which may effect the desirability of using response surfaces. It may be possible that artificial neural nets are more efficient in developing response surfaces than polynomial approximations which have been used in the past. This development is the concern of the work.

  9. Detection and location of pipe damage by artificial-neural-net-processed moire error maps

    NASA Astrophysics Data System (ADS)

    Grossman, Barry G.; Gonzalez, Frank S.; Blatt, Joel H.; Cahall, Scott C.

    1993-05-01

    A novel automated inspection technique to recognize, locate, and quantify damage is developed. This technique is based on two already existing technologies: video moire metrology and artificial neural networks. Contour maps generated by video moire techniques provide an accurate description of surface structure that can then be automated by means of neutral networks. Artificial neural networks offer an attractive solution to the automated interpretation problem because they can generalize from the learned samples and provide an intelligent response for similar patterns having missing or noisy data. Two dimensional video moire images of pipes with dents of different depths, at several rotations, were used to train a multilayer feedforward neural network by the backpropagation algorithm. The backpropagation network is trained to recognize and classify the video moire images according to the dent's depth. Once trained, the network outputs give an indication of the probability that a dent has been found, a depth estimate, and the axial location of the center of the dent. This inspection technique has been demonstrated to be a powerful tool for the automatic location and quantification of structural damage, as illustrated using dented pipes.

  10. Neural Network Development Tool (NETS)

    NASA Technical Reports Server (NTRS)

    Baffes, Paul T.

    1990-01-01

    Artificial neural networks formed from hundreds or thousands of simulated neurons, connected in manner similar to that in human brain. Such network models learning behavior. Using NETS involves translating problem to be solved into input/output pairs, designing network configuration, and training network. Written in C.

  11. A comparison of artificial neural net and inductive decision tree learning applied to the diagnosis of coronary artery disease

    SciTech Connect

    Silver, D.L.; Hurwitz, G.A.; Cradduck, T.D.

    1994-05-01

    A variety of artificial intelligence systems are available for applications within nuclear medicine. It is important to understand the strengths and weaknesses of these systems and the class of problems for which each is best. Two supervised machine learning systems, a back propagation neural network and an inductive decision tree, were applied to the classification of coronary artery disease given a set of diagnostic input parameters. A comparison indicates that both paradigms perform well depending upon the requirements of the user. We examined the setup complexity, learning and classification speed, training accuracy, ability to generalize to previously unseen cases, and the explanatory power of the internal representations generated by the learning systems. A database of 503 patient records composed of ten parameters was used for the analysis. The target response was a binary value of disease or no disease. The results indicate that the inductive decision tree learning system is the better choice for this class of problem. It is easier to setup and training takes less time. It has good explanatory power since it produces a printed decision tree of the internal representation of acquired knowledge. On the other hand, the artificial neural net provides better generalization for new test cases, and has greater classification accuracy.

  12. Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

    PubMed

    Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A

    2013-08-01

    As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

  13. A multi-views multi-learners approach towards dysarthric speech recognition using multi-nets artificial neural networks.

    PubMed

    Shahamiri, Seyed Reza; Salim, Siti Salwah Binti

    2014-09-01

    Automatic speech recognition (ASR) can be very helpful for speakers who suffer from dysarthria, a neurological disability that damages the control of motor speech articulators. Although a few attempts have been made to apply ASR technologies to sufferers of dysarthria, previous studies show that such ASR systems have not attained an adequate level of performance. In this study, a dysarthric multi-networks speech recognizer (DM-NSR) model is provided using a realization of multi-views multi-learners approach called multi-nets artificial neural networks, which tolerates variability of dysarthric speech. In particular, the DM-NSR model employs several ANNs (as learners) to approximate the likelihood of ASR vocabulary words and to deal with the complexity of dysarthric speech. The proposed DM-NSR approach was presented as both speaker-dependent and speaker-independent paradigms. In order to highlight the performance of the proposed model over legacy models, multi-views single-learner models of the DM-NSRs were also provided and their efficiencies were compared in detail. Moreover, a comparison among the prominent dysarthric ASR methods and the proposed one is provided. The results show that the DM-NSR recorded improved recognition rate by up to 24.67% and the error rate was reduced by up to 8.63% over the reference model.

  14. Refractory neural nets and vision

    NASA Astrophysics Data System (ADS)

    Fall, Thomas C.

    2014-02-01

    Biological understandings have served as the basis for new computational approaches. A prime example is artificial neural nets which are based on the biological understanding of the trainability of neural synapses. In this paper, we will investigate features of the biological vision system to see if they can also be exploited. These features are 1) the neuron's refractory period - the period of time after the neuron fires before it can fire again and 2) the ocular microtremor which moves the retinal neural array relative to the image. The short term memory due to the refractory period allows the before and after movement views to be compared. This paper will discuss the investigation of the implications of these two features.

  15. Quantum Neural Nets

    NASA Technical Reports Server (NTRS)

    Zak, Michail; Williams, Colin P.

    1997-01-01

    The capacity of classical neurocomputers is limited by the number of classical degrees of freedom which is roughly proportional to the size of the computer. By Contrast, a Hypothetical quantum neurocomputer can implement an exponentially large number of the degrees of freedom within the same size. In this paper an attempt is made to reconcile linear reversible structure of quantum evolution with nonlinear irreversible dynamics for neural nets.

  16. Neural nets on the MPP

    NASA Technical Reports Server (NTRS)

    Hastings, Harold M.; Waner, Stefan

    1987-01-01

    The Massively Parallel Processor (MPP) is an ideal machine for computer experiments with simulated neural nets as well as more general cellular automata. Experiments using the MPP with a formal model neural network are described. The results on problem mapping and computational efficiency apply equally well to the neural nets of Hopfield, Hinton et al., and Geman and Geman.

  17. Implementation of artificial neural networks with optics

    NASA Astrophysics Data System (ADS)

    Yu, Francis T. S.

    1999-04-01

    Optical implementation of artificial neural nets (ANNs) with electronically addressable liquid crystal televisions (LCTVs) are presented. The major advantages of the proposed ANNs must be the low cost and the flexibility to operate. To test the performance, several artificial neural net models have been implemented in the LCTV ANNs. These models include the Hopfield, Interpattern Association, Hetero-association, and Unsupervised ANNs. System design considerations and experimental demonstrates are provided.

  18. Template learning in morphological neural nets

    NASA Astrophysics Data System (ADS)

    Davidson, Jennifer L.; Sun, K.

    1991-07-01

    This paper presents an application of morphology neural networks to a template learning problem. Morphology neural networks are a nonlinear version of the familiar artificial neural networks. Typically, an artificial neural net is used to solve pattern classification problems One useful characterization of many neural network algorithms is the ability to 'learn' to respond correctly to new data based only on a selection of known data responses. For example, in the multilayer perceptron model, the 'learning' is a procedure whereby parameters are fed back from output to input neurons and the weights changed to give a better response. The morphological neural net in this paper solves a different type of image processing problem. Specifically, given an input image and an output image which corresponds to a dilated version of the input, one would like to determine what template produced the output. The problem corresponds to teaching the network to solve for the weights in a morphological net, as the weights are the template's values. A reasonable method has been investigated for the boolean case; in this paper results are presented for gray scale images. Image algebra has been shown to provide a succinct expression of neural networks algorithms and also to allow a generalization of neural networks, and thus the authors describe the algorithm in image algebra. The remainder of the paper gives a brief discussion of image algebra, the relationship of image algebra and neural networks, a recap of the dilation morphology neural network boolean for boolean images, and the generalization to grayscale data.

  19. SkyNet: An Efficient and Robust Algorithm for Training Artificial Neural Networks for Astrophysics and More

    NASA Astrophysics Data System (ADS)

    Graff, Philip; Feroz, F.; Hobson, M. P.; Lasenby, A. N.

    2013-01-01

    Artificial neural networks (NNs) are a method of computation loosely based on the structure of a brain. They consist of a group of interconnected nodes, which process information that they receive and then pass this product along to other nodes via weighted connections. A network is able “learn” a relationship between inputs and outputs given a set of training data and can then make predictions of the outputs for new input data. Many calculations in astrophysics are very computationally expensive. If a calculation needs to be performed a large number of times, then it can significantly slow down simulations and analysis. We set out to develop a generic algorithm for the training of NNs in order to facilitate and expedite these repeated difficult computations. We demonstrate the effectiveness of our algorithm for NN training in a few examples and then apply it to astrophysical problems.

  20. Weakly connected neural nets

    NASA Technical Reports Server (NTRS)

    Zak, Michail

    1990-01-01

    A new neural network architecture is proposed based upon effects of non-Lipschitzian dynamics. The network is fully connected, but these connections are active only during vanishingly short time periods. The advantages of this architecture are discussed.

  1. Generalized Adaptive Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1993-01-01

    Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

  2. Generalized Adaptive Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1993-01-01

    Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

  3. Trimaran Resistance Artificial Neural Network

    DTIC Science & Technology

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  4. [Artificial neural networks in Neurosciences].

    PubMed

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  5. Implementing neural nets with programmable logic

    NASA Technical Reports Server (NTRS)

    Vidal, Jacques J.

    1988-01-01

    Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.

  6. Implementing neural nets with programmable logic

    NASA Technical Reports Server (NTRS)

    Vidal, Jacques J.

    1988-01-01

    Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.

  7. Multifunctional hybrid optical/digital neural net

    NASA Astrophysics Data System (ADS)

    Casasent, David P.

    1990-08-01

    A multi-functional hybrid neural net is described. It is hybrid since it uses a digital hardware Hecht-Nielsen Corporation (HNC) neural net for adaptive learning and an optical neural net for on-line processing/classification. It is also hybrid in its combination of pattern recognition and neural net techniques. The system is multi-functional. It can function as an optimization and adaptive pattern recognition neural net as well as an auto and heteroassociative processor. I . W. JTRODUCTION Neural nets (NNs) have recently received enormous attention [1 -2] with increasing attention to the use of optical processors and a variety of new learning algorithms. Section 2 describes our hybrid NN with attention to Its fabrication and the role for optical and digital processors. Section 3 details Its use as an associative processor. Section 4 highlights is use in 3 optimization NN problems (a mixture NN a multitarget tracker (MTT) NN and a matrix inversion NN). Section 5 briefly notes it use as a production NN system and symbolic NN. Section 6 describes its use as an adaptive pattern recognition (PR) NN (that marries PR and NN techniques). 2. HYBRID ARCHITECTURE Figure 1 shows our basic hybrid NN [3]. The optical portion of the system is a matrix-vector (M-V) processor whose vector output P3 is the product of the vector at P1 and the matrix at P2. An HNC digital hardware NN is used during learning determine the interconnection weights forP2. If P2 is a spatial light modulator (SLM) its contents can be updated (using gated learning) from thedigital NN. The operations in most adaptive PR NN learning algorithms are sufficiently complex thatthey are best implemented digitally. In addition the learning operations required are often not well suited for optical realization for optimization NNs the weights are fixed and in adaptive learning learning is off-line and once completed the weights can often be fixed. Four gates are shown that determine the final output or the new P1

  8. Artificial intelligence: Deep neural reasoning

    NASA Astrophysics Data System (ADS)

    Jaeger, Herbert

    2016-10-01

    The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

  9. Are artificial neural networks black boxes?

    PubMed

    Benitez, J M; Castro, J L; Requena, I

    1997-01-01

    Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.

  10. Artificial nets from superconducting nanogranules

    NASA Astrophysics Data System (ADS)

    Ovchinnikov, Yu. N.; Kresin, V. Z.

    2012-06-01

    We show that a large transport current can flow through superconducting nets composed of nano-clusters. Although thermal and quantum fluctuations lead to a finite value of dissipation, this value can be very small in one- and two-dimensional systems for realistic parameters of the nanoclusters and distances between them. The value of the action for vortex tunneling at zero temperature can be made sufficiently large to make the dissipation negligibly small. We estimate the temperature T 0 of the transition from the thermal activation to quantum tunneling.

  11. Artificial neural networks in medicine

    SciTech Connect

    Keller, P.E.

    1994-07-01

    This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.

  12. Neural Net Safety Monitor Design

    NASA Technical Reports Server (NTRS)

    Larson, Richard R.

    2007-01-01

    The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The

  13. Introduction to Concepts in Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  14. Introduction to Concepts in Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  15. 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.

  16. Devices and circuits for nanoelectronic implementation of artificial neural networks

    NASA Astrophysics Data System (ADS)

    Turel, Ozgur

    Biological neural networks perform complicated information processing tasks at speeds better than conventional computers based on conventional algorithms. This has inspired researchers to look into the way these networks function, and propose artificial networks that mimic their behavior. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed or the complexity of a human brain. Nanoelectronics, with high density and low power dissipation that it provides, may be used in developing more efficient artificial neural networks. This work consists of two major contributions in this direction. First is the proposal of the CMOL concept, hybrid CMOS-molecular hardware [1-8]. CMOL may circumvent most of the problems in posed by molecular devices, such as low yield, vet provide high active device density, ˜1012/cm 2. The second contribution is CrossNets, artificial neural networks that are based on CMOL. We showed that CrossNets, with their fault tolerance, exceptional speed (˜ 4 to 6 orders of magnitude faster than biological neural networks) can perform any task any artificial neural network can perform. Moreover, there is a hope that if their integration scale is increased to that of human cerebral cortex (˜ 1010 neurons and ˜ 1014 synapses), they may be capable of performing more advanced tasks.

  17. Neural net based MRAC for a class of nonlinear plants.

    PubMed

    Ahmed, M S

    2000-01-01

    A neural net based state feedback model reference adaptive control scheme is presented for a class of nonlinear plants. The proposed scheme employs a time varying pseudo-linear state feedback control, where the state feedback gain being generated from the output of artificial neural networks. The plant need not be in a feedback linearizable form. Both regulation and tracking control have been considered. Global stability of the scheme has been proved through Lyapunov theory. The extension of the scheme to MIMO plants is also included. Simulation studies have been conducted on an industrial robot to validate and illustrate the proposed method.

  18. Plant Growth Models Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  19. Plant Growth Models Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  20. The power of neural nets

    NASA Technical Reports Server (NTRS)

    Ryan, J. P.; Shah, B. H.

    1987-01-01

    Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

  1. Optical Neural Nets for Scene Analysis

    DTIC Science & Technology

    1989-10-23

    Report DARPA/SEMI/ 1089 OPTICAL NEURAL NETS FOR SCENE ANALYSIS W David Casasent (PI) DTIC 00 Carnegie Mellon University ELECT E & , Center for...REPORT NUMBER(S) S. MONITORING ORGANIZATION REPORT NUMBER(S) DARPA/Semi/ 1089 6a. NAME OF PERFORMING ORGANIZATION 6b. OFFICE SYMBOL 7a NAME OF MONITORING

  2. Creativity in design and artificial neural networks

    SciTech Connect

    Neocleous, C.C.; Esat, I.I.; Schizas, C.N.

    1996-12-31

    The creativity phase is identified as an integral part of the design phase. The characteristics of creative persons which are relevant to designing artificial neural networks manifesting aspects of creativity, are identified. Based on these identifications, a general framework of artificial neural network characteristics to implement such a goal are proposed.

  3. Isolated Speech Recognition Using Artificial Neural Networks

    DTIC Science & Technology

    2007-11-02

    In this project Artificial Neural Networks are used as research tool to accomplish Automated Speech Recognition of normal speech. A small size...the first stage of this work are satisfactory and thus the application of artificial neural networks in conjunction with cepstral analysis in isolated word recognition holds promise.

  4. Modular, Hierarchical Learning By Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  5. Modular, Hierarchical Learning By Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  6. Neural-net Processed Electronic Holography for Rotating Machines

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    2003-01-01

    This report presents the results of an R&D effort to apply neural-net processed electronic holography to NDE of rotors. Electronic holography was used to generate characteristic patterns or mode shapes of vibrating rotors and rotor components. Artificial neural networks were trained to identify damage-induced changes in the characteristic patterns. The development and optimization of a neural-net training method were the most significant contributions of this work, and the training method and its optimization are discussed in detail. A second positive result was the assembly and testing of a fiber-optic holocamera. A major disappointment was the inadequacy of the high-speed-holography hardware selected for this effort, but the use of scaled holograms to match the low effective resolution of an image intensifier was one interesting attempt to compensate. This report also discusses in some detail the physics and environmental requirements for rotor electronic holography. The major conclusions were that neural-net and electronic-holography inspections of stationary components in the laboratory and the field are quite practical and worthy of continuing development, but that electronic holography of moving rotors is still an expensive high-risk endeavor.

  7. Artificial neural networks in neurosurgery.

    PubMed

    Azimi, Parisa; Mohammadi, Hasan Reza; Benzel, Edward C; Shahzadi, Sohrab; Azhari, Shirzad; Montazeri, Ali

    2015-03-01

    Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  8. Semantic Interpretation of An Artificial Neural Network

    DTIC Science & Technology

    1995-12-01

    success for stock market analysis/prediction is artificial neural networks. However, knowledge embedded in the neural network is not easily translated...interpret neural network knowledge. The first, called Knowledge Math, extends the use of connection weights, generating rules for general (i.e. non-binary

  9. Learning in Artificial Neural Systems

    NASA Technical Reports Server (NTRS)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  10. The use of artificial neural networks in experimental data acquisition and aerodynamic design

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J., Jr.

    1991-01-01

    It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

  11. Neural Net Gains Estimation Based on an Equivalent Model

    PubMed Central

    Aguilar Cruz, Karen Alicia; Medel Juárez, José de Jesús; Fernández Muñoz, José Luis; Esmeralda Vigueras Velázquez, Midory

    2016-01-01

    A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system. PMID:27366146

  12. Neural Net Gains Estimation Based on an Equivalent Model.

    PubMed

    Aguilar Cruz, Karen Alicia; Medel Juárez, José de Jesús; Fernández Muñoz, José Luis; Esmeralda Vigueras Velázquez, Midory

    2016-01-01

    A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system.

  13. Neural net approach to predictive vector quantization

    NASA Astrophysics Data System (ADS)

    Mohsenian, Nader; Nasrabadi, Nasser M.

    1992-11-01

    A new predictive vector quantization (PVQ) technique, capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks of pixels, is introduced. Two different classes of neural nets form the components of the PVQ scheme. A multi-layer perceptron is embedded in the predictive component of the compression system. This neural network, using the non-linearity condition associated with its processing units, can perform as a non-linear vector predictor. The second component of the PVQ scheme vector quantizes (VQ) the residual vector that is formed by subtracting the output of the perceptron from the original wave-pattern. Kohonen Self-Organizing Feature Map (KSOFM) was utilized as a neural network clustering algorithm to design the codebook for the VQ technique. Coding results are presented for monochrome 'still' images.

  14. Artificial neural network and medicine.

    PubMed

    Khan, Z H; Mohapatra, S K; Khodiar, P K; Ragu Kumar, S N

    1998-07-01

    The introduction of human brain functions such as perception and cognition into the computer has been made possible by the use of Artificial Neural Network (ANN). ANN are computer models inspired by the structure and behavior of neurons. Like the brain, ANN can recognize patterns, manage data and most significantly, learn. This learning ability, not seen in other computer models simulating human intelligence, constantly improves its functional accuracy as it keeps on performing. Experience is as important for an ANN as it is for man. It is being increasingly used to supplement and even (may be) replace experts, in medicine. However, there is still scope for improvement in some areas. Its ability to classify and interpret various forms of medical data comes as a helping hand to clinical decision making in both diagnosis and treatment. Treatment planning in medicine, radiotherapy, rehabilitation, etc. is being done using ANN. Morbidity and mortality prediction by ANN in different medical situations can be very helpful for hospital management. ANN has a promising future in fundamental research, medical education and surgical robotics.

  15. Artificial Astrocytes Improve Neural Network Performance

    PubMed Central

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  16. Artificial Neural Networks and Instructional Technology.

    ERIC Educational Resources Information Center

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  17. Artificial Neural Networks and Instructional Technology.

    ERIC Educational Resources Information Center

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  18. Artificial astrocytes improve neural network performance.

    PubMed

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  19. Document analysis with neural net circuits

    NASA Technical Reports Server (NTRS)

    Graf, Hans Peter

    1994-01-01

    Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

  20. Macro cell placement with neural net algorithms

    NASA Astrophysics Data System (ADS)

    Storti-Gajani, Giancarlo

    Placement of VLSI (Very Large Scale Integration) macro cells is one of the hard problems encountered in the process of integrated circuits design. Since the problem is essentially NP-complete a solution must be searched for with the aid of heuristics using, maybe, non deterministic strategies. A new algorithm for cell preplacement based on neural nets that may be very well extended to find solution of the final placement problem is presented. Simulations for the part of the algorithm concerning preplacement are carried out on several different examples giving always a sharply decreasing cost function (where cost is evaluated essentially on total length of wires given a rectangular boundary). The direct mapping between neural units and VLSI blocks that is adopted in the algorithm makes the extension to the final placement problem quite simple. Simulation programs are implemented in a interpreted mathematical simulation language and a C language implementation is currently under way.

  1. Acute appendicitis diagnosis using artificial neural networks.

    PubMed

    Park, Sung Yun; Kim, Sung Min

    2015-01-01

    Artificial neural networks is one of pattern analyzer method which are rapidly applied on a bio-medical field. The aim of this research was to propose an appendicitis diagnosis system using artificial neural networks (ANNs). Data from 801 patients of the university hospital in Dongguk were used to construct artificial neural networks for diagnosing appendicitis and acute appendicitis. A radial basis function neural network structure (RBF), a multilayer neural network structure (MLNN), and a probabilistic neural network structure (PNN) were used for artificial neural network models. The Alvarado clinical scoring system was used for comparison with the ANNs. The accuracy of the RBF, PNN, MLNN, and Alvarado was 99.80%, 99.41%, 97.84%, and 72.19%, respectively. The area under ROC (receiver operating characteristic) curve of RBF, PNN, MLNN, and Alvarado was 0.998, 0.993, 0.985, and 0.633, respectively. The proposed models using ANNs for diagnosing appendicitis showed good performances, and were significantly better than the Alvarado clinical scoring system (p < 0.001). With cooperation among facilities, the accuracy for diagnosing this serious health condition can be improved.

  2. An artificial neural network for wavelet steganalysis

    NASA Astrophysics Data System (ADS)

    Davidson, Jennifer; Bergman, Clifford; Bartlett, Eric

    2005-08-01

    Hiding messages in image data, called steganography, is used for both legal and illicit purposes. The detection of hidden messages in image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics personnel. Automating the detection of hidden messages is a requirement, since the shear amount of image data stored on computers or websites makes it impossible for a person to investigate each image separately. This paper describes research on a prototype software system that automatically classifies an image as having hidden information or not, using a sophisticated artificial neural network (ANN) system. An ANN software package, the ISU ACL NetWorks Toolkit, is trained on a selection of image features that distinguish between stego and nonstego images. The novelty of this ANN is that it is a blind classifier that gives more accurate results than previous systems. It can detect messages hidden using a variety of different types of embedding algorithms. A Graphical User Interface (GUI) combines the ANN, feature selection, and embedding algorithms into a prototype software package that is not currently available to the cyber forensics community.

  3. Optimization of Training Sets for Neural-Net Processing of Characteristic Patterns from Vibrating Solids

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    2001-01-01

    Artificial neural networks have been used for a number of years to process holography-generated characteristic patterns of vibrating structures. This technology depends critically on the selection and the conditioning of the training sets. A scaling operation called folding is discussed for conditioning training sets optimally for training feed-forward neural networks to process characteristic fringe patterns. Folding allows feed-forward nets to be trained easily to detect damage-induced vibration-displacement-distribution changes as small as 10 nm. A specific application to aerospace of neural-net processing of characteristic patterns is presented to motivate the conditioning and optimization effort.

  4. Forecasting Jet Fuel Prices Using Artificial Neural Networks.

    DTIC Science & Technology

    1995-03-01

    Artificial neural networks provide a new approach to commodity forecasting that does not require algorithm or rule development. Neural networks have...NeuralWare, more people can take advantage of the power of artificial neural networks . This thesis provides an introduction to neural networks, and reviews

  5. Artificial Neural Network Analysis System

    DTIC Science & Technology

    2007-11-02

    Target detection, multi-target tracking and threat identification of ICBM and its warheads by sensor fusion and data fusion of sensors in a fuzzy neural network system based on the compound eye of a fly.

  6. Neural-net based real-time economic dispatch for thermal power plants

    SciTech Connect

    Djukanovic, M.; Milosevic, B.; Calovic, M.; Sobajic, D.J.

    1996-12-01

    This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

  7. Neural net prediction of tokamak plasma disruptions

    NASA Astrophysics Data System (ADS)

    Hernandez, J. V.; Lin, Z.; Horton, W.; Vannucci, A.; McCool, S. C.

    1994-10-01

    The computation based on neural net algorithms in predicting minor and major disruptions in TEXT tokamak discharges has been performed. Future values of the fluctuating magnetic signal are predicted based on L past values of the magnetic fluctuation signal, measured by a single Mirnov coil. The time step used (= 0.04ms) corresponds to the experimental data sampling rate. Two kinds of approaches are adopted for the task, the contiguous future prediction and the multi-timescale prediction. Results are shown for comparison. Both networks are trained through the back-propagation algorithm with inertial terms. The degree of this success indicates that the magnetic fluctuations associated with tokamak disruptions may be characterized by a relatively low-dimensional dynamical system.

  8. Cotton genotypes selection through artificial neural networks.

    PubMed

    Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

    2017-09-27

    Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

  9. Extraction of shoreline features by neural nets and image processing

    SciTech Connect

    Ryan, T.W.; Sementilli, P.J.; Yuen, P.; Hunt, B.R. )

    1991-07-01

    This paper demonstrates the capability of using neural networks as a tool for delineation of shorelines. The neural nets used are multilayer perceptrons, i.e., feed-forward nets with one or more layers of nodes between the input and output nodes. The back-propagation learning algorithm is used as the adaptation rule. 24 refs.

  10. Psychometric Measurement Models and Artificial Neural Networks

    ERIC Educational Resources Information Center

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  11. Psychometric Measurement Models and Artificial Neural Networks

    ERIC Educational Resources Information Center

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  12. Artificial neural network simulation of battery performance

    SciTech Connect

    O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.

    1998-12-31

    Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

  13. [Application of artificial neural networks in infectious diseases].

    PubMed

    Xu, Jun-fang; Zhou, Xiao-nong

    2011-02-28

    With the development of information technology, artificial neural networks has been applied to many research fields. Due to the special features such as nonlinearity, self-adaptation, and parallel processing, artificial neural networks are applied in medicine and biology. This review summarizes the application of artificial neural networks in the relative factors, prediction and diagnosis of infectious diseases in recent years.

  14. Chaotic time series prediction using artificial neural networks

    SciTech Connect

    Bartlett, E.B.

    1991-12-31

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  15. Chaotic time series prediction using artificial neural networks

    SciTech Connect

    Bartlett, E.B.

    1991-01-01

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  16. Porosity Log Prediction Using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Dwi Saputro, Oki; Lazuardi Maulana, Zulfikar; Dzar Eljabbar Latief, Fourier

    2016-08-01

    Well logging is important in oil and gas exploration. Many physical parameters of reservoir is derived from well logging measurement. Geophysicists often use well logging to obtain reservoir properties such as porosity, water saturation and permeability. Most of the time, the measurement of the reservoir properties are considered expensive. One of method to substitute the measurement is by conducting a prediction using artificial neural network. In this paper, artificial neural network is performed to predict porosity log data from other log data. Three well from ‘yy’ field are used to conduct the prediction experiment. The log data are sonic, gamma ray, and porosity log. One of three well is used as training data for the artificial neural network which employ the Levenberg-Marquardt Backpropagation algorithm. Through several trials, we devise that the most optimal input training is sonic log data and gamma ray log data with 10 hidden layer. The prediction result in well 1 has correlation of 0.92 and mean squared error of 5.67 x10-4. Trained network apply to other well data. The result show that correlation in well 2 and well 3 is 0.872 and 0.9077 respectively. Mean squared error in well 2 and well 3 is 11 x 10-4 and 9.539 x 10-4. From the result we can conclude that sonic log and gamma ray log could be good combination for predicting porosity with neural network.

  17. Real-time applications of neural nets

    SciTech Connect

    Spencer, J.E.

    1989-05-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs.

  18. Real-time applications of neural nets

    SciTech Connect

    Spencer, J.E. )

    1989-10-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas e.g. improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. In this paper, such issues are considered, examples given and possibilities discussed.

  19. Livermore Big Artificial Neural Network Toolkit

    SciTech Connect

    Essen, Brian Van; Jacobs, Sam; Kim, Hyojin; Dryden, Nikoli; Moon, Tim

    2016-07-01

    LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

  20. Development of programmable artificial neural networks

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J.

    1993-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  1. Non-Intrusive Gaze Tracking Using Artificial Neural Networks

    DTIC Science & Technology

    1994-01-05

    Artificial Neural Networks Shumeet Baluja & Dean...this paper appear in: Baluja, S. & Pomerleau, D.A. "Non-Intrusive Gaze Tracking Using Artificial Neural Networks ", Advances in Neural Information...document hLc-s been opproved t0T 011bhiC leleWOe cad ý’ir/4 its di stT-b’ution Ls •_nii•ite6. - Keywords Gaze Tracking, Artificial Neural Networks ,

  2. Analysis of Wideband Beamformers Designed with Artificial Neural Networks

    DTIC Science & Technology

    1990-12-01

    TECHNICAL REPORT 0-90-1 ANALYSIS OF WIDEBAND BEAMFORMERS DESIGNED WITH ARTIFICIAL NEURAL NETWORKS by Cary Cox Instrumentation Services Division...included. A briel tutorial on beamformers and neural networks is also provided. 14. SUBJECT TERMS 15, NUMBER OF PAGES Artificial neural networks Fecdforwa:,l...Beamformers Designed with Artificial Neural Networks ". The study was conducted under the general supervision of Messrs. George P. Bonner, Chief

  3. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, L.J.; Keller, P.E.

    1997-10-28

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

  4. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  5. Neural net applied to anthropological material: a methodical study on the human nasal skeleton.

    PubMed

    Prescher, Andreas; Meyers, Anne; Gerf von Keyserlingk, Diedrich

    2005-07-01

    A new information processing method, an artificial neural net, was applied to characterise the variability of anthropological features of the human nasal skeleton. The aim was to find different types of nasal skeletons. A neural net with 15*15 nodes was trained by 17 standard anthropological parameters taken from 184 skulls of the Aachen collection. The trained neural net delivers its classification in a two-dimensional map. Different types of noses were locally separated within the map. Rare and frequent types may be distinguished after one passage of the complete collection through the net. Statistical descriptive analysis, hierarchical cluster analysis, and discriminant analysis were applied to the same data set. These parallel applications allowed comparison of the new approach to the more traditional ones. In general the classification by the neural net is in correspondence with cluster analysis and discriminant analysis. However, it goes beyond these classifications because of the possibility of differentiating the types in multi-dimensional dependencies. Furthermore, places in the map are kept blank for intermediate forms, which may be theoretically expected, but were not included in the training set. In conclusion, the application of a neural network is a suitable method for investigating large collections of biological material. The gained classification may be helpful in anatomy and anthropology as well as in forensic medicine. It may be used to characterise the peculiarity of a whole set as well as to find particular cases within the set.

  6. Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    2004-01-01

    Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

  7. Artificial neural network intelligent method for prediction

    NASA Astrophysics Data System (ADS)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  8. Use of artifical neural nets to predict permeability in Hugoton Field

    SciTech Connect

    Thompson, K.A.; Franklin, M.H.; Olson, T.M. )

    1996-01-01

    One of the most difficult tasks in petrophysics is establishing a quantitative relationship between core permeability and wireline logs. This is a tough problem in Hugoton Field, where a complicated mix of carbonates and clastics further obscure the correlation. One can successfully model complex relationships such as permeability-to-logs using artificial neural networks. Mind and Vision, Inc.'s neural net software was used because of its orientation toward depth-related data (such as logs) and its ability to run on a variety of log analysis platforms. This type of neural net program allows the expert geologist to select a few (10-100) points of control to train the [open quotes]brainstate[close quotes] using logs as predicters and core permeability as [open quotes]truth[close quotes]. In Hugoton Field, the brainstate provides an estimate of permeability at each depth in 474 logged wells. These neural net-derived permeabilities are being used in reservoir characterization models for fluid saturations. Other applications of this artificial neural network technique include deterministic relationships of logs to: core lithology, core porosity, pore type, and other wireline logs (e.g., predicting a sonic log from a density log).

  9. Use of artifical neural nets to predict permeability in Hugoton Field

    SciTech Connect

    Thompson, K.A.; Franklin, M.H.; Olson, T.M.

    1996-12-31

    One of the most difficult tasks in petrophysics is establishing a quantitative relationship between core permeability and wireline logs. This is a tough problem in Hugoton Field, where a complicated mix of carbonates and clastics further obscure the correlation. One can successfully model complex relationships such as permeability-to-logs using artificial neural networks. Mind and Vision, Inc.`s neural net software was used because of its orientation toward depth-related data (such as logs) and its ability to run on a variety of log analysis platforms. This type of neural net program allows the expert geologist to select a few (10-100) points of control to train the {open_quotes}brainstate{close_quotes} using logs as predicters and core permeability as {open_quotes}truth{close_quotes}. In Hugoton Field, the brainstate provides an estimate of permeability at each depth in 474 logged wells. These neural net-derived permeabilities are being used in reservoir characterization models for fluid saturations. Other applications of this artificial neural network technique include deterministic relationships of logs to: core lithology, core porosity, pore type, and other wireline logs (e.g., predicting a sonic log from a density log).

  10. Pattern recognition, neural networks, and artificial intelligence

    NASA Astrophysics Data System (ADS)

    Bezdek, James C.

    1991-03-01

    We write about the relationship between numerical patten recognition and neural-like computation networks. Extensive research that proposes the use of neural models for a wide variety of applications has been conducted in the past few years. Sometimes justification for investigating the potential of neural nets (NNs) is obvious. On the other hand, current enthusiasm for this approach has also led to the use of neural models when the apparent rationale for their use has been justified by what is best described as 'feeding frenzy'. In this latter instance there is at times concomitant lack of concern about many 'side issues' connected with algorithms (e.g., complexity, convergence, stability, robustness and performance validation) that need attention before any computational model becomes part of an operation system. These issues are examined with a view towards guessing how best to integrate and exploit the promise of the neural approach with there efforts aimed at advancing the art and science of pattern recognition and its applications in fielded systems in the next decade.

  11. Synchronous machine steady-state stability analysis using an artificial neural network

    SciTech Connect

    Chen, C.R.; Hsu, Y.Y. . Dept. of Electrical Engineering)

    1991-03-01

    A new type of artificial neural network is proposed for the steady-state stability analysis of a synchronous generator. In the developed artificial neutral network, those system variables which play an important role in steady-state stability such as generator outputs and power system stabilizer parameters are employed as the inputs. The output of the neural net provides the information on steady-state stability. Once the connection weights of the neural network have been learned using a set of training data derived off-line, the neural net can be applied to analyze the steady-state stability of the system time. To demonstrate the effectiveness of the proposed neural net, steady-state stability analysis is performed on a synchronous generator connected to a large power system. It is found that the proposed neural net requires much less training time than the multilayer feedforward network with backpropagation-momentum learning algorithm. It is also concluded from the test results that correct stability assessment can be achieved by the neural network.

  12. Improved Landmine Detection by Complex-Valued Artificial Neural Networks

    DTIC Science & Technology

    2002-12-04

    IMPROVED LANDMINE DETECTION BY COMPLEX-VALUED ARTIFICIAL NEURAL NETWORKS Research was Sponsored by: U. S. ARMY RESEARCH OFFICE Program Manager... artificial neural networks in conjunction with fuzzy logic for improved system performance over and above the good results already attained are...of detecting mines. One of the more promising avenues of research in this area involves the use of artificial neural networks [3]. More specifically

  13. An Evaluation of Artificial Neural Network Modeling for Manpower Analysis

    DTIC Science & Technology

    1993-09-01

    This thesis evaluates the capabilities of artificial neural networks in forecasting the take-rates of the Voluntary Separations Incentive/Special...Separations Benefit (VSI/SSB) programs for male, Marine Corps Enlisted Personnel in the grades of E-5 and E-6. The Artificial Neural Networks models are...results indicate that artificial neural networks provide forecasting results at least as good as, if not better than, those obtained using classical

  14. Molnets: An Artificial Chemistry Based on Neural Networks

    NASA Technical Reports Server (NTRS)

    Colombano, Silvano; Luk, Johnny; Segovia-Juarez, Jose L.; Lohn, Jason; Clancy, Daniel (Technical Monitor)

    2002-01-01

    The fundamental problem in the evolution of matter is to understand how structure-function relationships are formed and increase in complexity from the molecular level all the way to a genetic system. We have created a system where structure-function relationships arise naturally and without the need of ad hoc function assignments to given structures. The idea was inspired by neural networks, where the structure of the net embodies specific computational properties. In this system networks interact with other networks to create connections between the inputs of one net and the outputs of another. The newly created net then recomputes its own synaptic weights, based on anti-hebbian rules. As a result some connections may be cut, and multiple nets can emerge as products of a 'reaction'. The idea is to study emergent reaction behaviors, based on simple rules that constitute a pseudophysics of the system. These simple rules are parameterized to produce behaviors that emulate chemical reactions. We find that these simple rules show a gradual increase in the size and complexity of molecules. We have been building a virtual artificial chemistry laboratory for discovering interesting reactions and for testing further ideas on the evolution of primitive molecules. Some of these ideas include the potential effect of membranes and selective diffusion according to molecular size.

  15. Molnets: An Artificial Chemistry Based on Neural Networks

    NASA Technical Reports Server (NTRS)

    Colombano, Silvano; Luk, Johnny; Segovia-Juarez, Jose L.; Lohn, Jason; Clancy, Daniel (Technical Monitor)

    2002-01-01

    The fundamental problem in the evolution of matter is to understand how structure-function relationships are formed and increase in complexity from the molecular level all the way to a genetic system. We have created a system where structure-function relationships arise naturally and without the need of ad hoc function assignments to given structures. The idea was inspired by neural networks, where the structure of the net embodies specific computational properties. In this system networks interact with other networks to create connections between the inputs of one net and the outputs of another. The newly created net then recomputes its own synaptic weights, based on anti-hebbian rules. As a result some connections may be cut, and multiple nets can emerge as products of a 'reaction'. The idea is to study emergent reaction behaviors, based on simple rules that constitute a pseudophysics of the system. These simple rules are parameterized to produce behaviors that emulate chemical reactions. We find that these simple rules show a gradual increase in the size and complexity of molecules. We have been building a virtual artificial chemistry laboratory for discovering interesting reactions and for testing further ideas on the evolution of primitive molecules. Some of these ideas include the potential effect of membranes and selective diffusion according to molecular size.

  16. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1995-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

  17. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1995-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

  18. A Novel Higher Order Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Xu, Shuxiang

    2010-05-01

    In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

  19. Digital Image Compression Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Serra-Ricart, M.; Garrido, L.; Gaitan, V.; Aloy, A.

    1993-01-01

    The problem of storing, transmitting, and manipulating digital images is considered. Because of the file sizes involved, large amounts of digitized image information are becoming common in modern projects. Our goal is to described an image compression transform coder based on artificial neural networks techniques (NNCTC). A comparison of the compression results obtained from digital astronomical images by the NNCTC and the method used in the compression of the digitized sky survey from the Space Telescope Science Institute based on the H-transform is performed in order to assess the reliability of the NNCTC.

  20. Automation of Some Operations of a Wind Tunnel Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Buggele, Alvin E.

    1996-01-01

    Artificial neural networks were used successfully to sequence operations in a small, recently modernized, supersonic wind tunnel at NASA-Lewis Research Center. The neural nets generated correct estimates of shadowgraph patterns, pressure sensor readings and mach numbers for conditions occurring shortly after startup and extending to fully developed flow. Artificial neural networks were trained and tested for estimating: sensor readings from shadowgraph patterns, shadowgraph patterns from shadowgraph patterns and sensor readings from sensor readings. The 3.81 by 10 in. (0.0968 by 0.254 m) tunnel was operated with its mach 2.0 nozzle, and shadowgraph was recorded near the nozzle exit. These results support the thesis that artificial neural networks can be combined with current workstation technology to automate wind tunnel operations.

  1. Automation of Some Operations of a Wind Tunnel Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Buggele, Alvin E.

    1996-01-01

    Artificial neural networks were used successfully to sequence operations in a small, recently modernized, supersonic wind tunnel at NASA-Lewis Research Center. The neural nets generated correct estimates of shadowgraph patterns, pressure sensor readings and mach numbers for conditions occurring shortly after startup and extending to fully developed flow. Artificial neural networks were trained and tested for estimating: sensor readings from shadowgraph patterns, shadowgraph patterns from shadowgraph patterns and sensor readings from sensor readings. The 3.81 by 10 in. (0.0968 by 0.254 m) tunnel was operated with its mach 2.0 nozzle, and shadowgraph was recorded near the nozzle exit. These results support the thesis that artificial neural networks can be combined with current workstation technology to automate wind tunnel operations.

  2. [Use of artificial neural networks in clinical psychology and psychiatry].

    PubMed

    Starzomska, Małgorzata

    2003-01-01

    Artificial neural networks make a highly specialised tools in data transformation. The human brain has become an inspiration for the makers of artificial neural networks. Although even though artificial neural networks are more frequently used in areas like financial analysis, marketing studies or economical modelling, their application in psychology and medicine has given a lot of promising and fascinating discoveries. It is worth that artificial neurol networks are successfully used in the diagnosis and etiopathogenesis description of various psychiatric disorders such as eating disorders, compulsions, depression or schizophrenia. To sum up, artificial neural networks offer a very promising option of research methodology for modern clinical psychology and psychiatry. The aim of this article is only an illustration of the applications of artificial neural networks in clinical psychology and psychiatry.

  3. Pruning artificial neural networks using neural complexity measures.

    PubMed

    Jorgensen, Thomas D; Haynes, Barry P; Norlund, Charlotte C F

    2008-10-01

    This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

  4. On Improved Least Squares Regression and Artificial Neural Network Meta-Models for Simulation via Control Variates

    DTIC Science & Technology

    2016-09-15

    27 5 Taxonomy of neural net architectures. [50...171 41 Multilayer perceptron artificial neural network with bias . . . . . . . . . . . . 172 xiii List of Tables Table Page 1 Taxonomy of...effort was followed by Wilson & Rubinstein in 1984 with two comprehensive VRT reviews [130] and [129] where he introduces a VRT taxonomy to better classify

  5. Functional expansion representations of artificial neural networks

    NASA Technical Reports Server (NTRS)

    Gray, W. Steven

    1992-01-01

    In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

  6. Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

    1998-01-01

    The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

  7. Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

    1997-01-01

    The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

  8. Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

    1998-01-01

    The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

  9. Using chaotic neural nets to compress, store, and transmit information

    NASA Astrophysics Data System (ADS)

    Basti, Gianfranco; Perrone, Antonio L.; Cocciolo, Paola

    1994-03-01

    In order to find a very efficient technique to compress, store, and transmit to earth information from a satellite we developed a scheme of chaotic neural net using a new technique of extraction of unstable orbits within a chaotic attractor without applying classical embedding dimensions. We illustrate this technique both from the theoretical and the experimental standpoint. From the theoretical standpoint we show that by this extraction technique it is possible to perform a series expansion of a chaotic dynamics directly through all its composing cycles. Finally, we show how to apply these new possibilities deriving from our new technique of chaos detection, characterization, and stabilization to design a chaotic neural net. Because it is possible to profit by all the skeleton of unstable periodic orbits (i.e., all the inner frequencies) characterizing a chaotic attractor to store information, this net can in principle display an exponential increasing of memory capacity with respect to classical attractor nets.

  10. Analog Very Large Scale Integration (VLSI) Implementations of Artificial Neural Networks

    DTIC Science & Technology

    1992-09-01

    There has been a recent resurgence of interest in the multi- disciplinary field of artificial neural networks . Artificial neural networks , originally...e.g., backpropagation, hopfield, bidirectional associative memories, etc.). Artificial Neural Networks , Analog VLSI.

  11. Speech Recognition Using Neural Nets and Dynamic Time Warping

    DTIC Science & Technology

    1988-12-01

    long did it take you to find the ON/OFF button? Imagine simply saying, "Stereo, power on". Considering the vastly more complicated process of...Figure 3.6 shows the normalization process in terms of a unit hypersphere. The second advantage of energy normalization is that neural net processing of...resulted in overall best p,-rformnace. it is used to train all later nets. Dynamic Time Warping Dynanic time warping tests wcre performed over a long period

  12. A real time neural net estimator of fatigue life

    NASA Technical Reports Server (NTRS)

    Troudet, T.; Merrill, W.

    1990-01-01

    A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines.

  13. Optical neural net for classifying imaging spectrometer data

    NASA Technical Reports Server (NTRS)

    Barnard, Etienne; Casasent, David P.

    1989-01-01

    The problem of determining the composition of an unknown input mixture from its measured spectrum, given the spectra of a number of elements, is studied. The Hopfield minimization procedure was used to express the determination of the compositions as a problem suitable for solution by neural nets. A mathematical description of the problem was developed and used as a basis for a neural network solution and an optical implementation.

  14. Application of artificial neural networks to the design optimization of aerospace structural components

    NASA Technical Reports Server (NTRS)

    Berke, Laszlo; Patnaik, Surya N.; Murthy, Pappu L. N.

    1993-01-01

    The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

  15. Programmable synaptic devices for electronic neural nets

    NASA Technical Reports Server (NTRS)

    Moopenn, A.; Thakoor, A. P.

    1990-01-01

    The architecture, design, and operational characteristics of custom VLSI and thin film synaptic devices are described. The devices include CMOS-based synaptic chips containing 1024 reprogrammable synapses with a 6-bit dynamic range, and nonvolatile, write-once, binary synaptic arrays based on memory switching in hydrogenated amorphous silicon films. Their suitability for embodiment of fully parallel and analog neural hardware is discussed. Specifically, a neural network solution to an assignment problem of combinatorial global optimization, implemented in fully parallel hardware using the synaptic chips, is described. The network's ability to provide optimal and near optimal solutions over a time scale of few neuron time constants has been demonstrated and suggests a speedup improvement of several orders of magnitude over conventional search methods.

  16. Programmable synaptic devices for electronic neural nets

    NASA Technical Reports Server (NTRS)

    Moopenn, A.; Thakoor, A. P.

    1990-01-01

    The architecture, design, and operational characteristics of custom VLSI and thin film synaptic devices are described. The devices include CMOS-based synaptic chips containing 1024 reprogrammable synapses with a 6-bit dynamic range, and nonvolatile, write-once, binary synaptic arrays based on memory switching in hydrogenated amorphous silicon films. Their suitability for embodiment of fully parallel and analog neural hardware is discussed. Specifically, a neural network solution to an assignment problem of combinatorial global optimization, implemented in fully parallel hardware using the synaptic chips, is described. The network's ability to provide optimal and near optimal solutions over a time scale of few neuron time constants has been demonstrated and suggests a speedup improvement of several orders of magnitude over conventional search methods.

  17. 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.

  18. Sleep scoring using artificial neural networks.

    PubMed

    Ronzhina, Marina; Janoušek, Oto; Kolářová, Jana; Nováková, Marie; Honzík, Petr; Provazník, Ivo

    2012-06-01

    Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved - next to other classification methods - by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.

  19. Mesh deformation based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Stadler, Domen; Kosel, Franc; Čelič, Damjan; Lipej, Andrej

    2011-09-01

    In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.

  20. Artificial neural network circuits with Josephson devices

    SciTech Connect

    Harada, Y.; Goto, E. )

    1991-03-01

    This article describes a new approach of Josephson devices for computer applications. With an artificial neural network scheme Josephson devices is expected to develop a new paradigm for future computer systems. Here the authors discuss circuit configuration for a neuron with Josephson devices. The authors proposed a combination of a variable bias source and Josephson devices for a synapse circuit. The bias source signal is steered by the Josephson device input signal and becomes the synapse output signal. These output signals are summed up at the specific resistor or inductor to produce the weighted sum of Josephson devices input signals. According to the error signal, the bias source value is corrected. This corresponds to the learning procedure.

  1. Artificial neural network for multifunctional areas.

    PubMed

    Riccioli, Francesco; El Asmar, Toufic; El Asmar, Jean-Pierre; Fagarazzi, Claudio; Casini, Leonardo

    2016-01-01

    The issues related to the appropriate planning of the territory are particularly pronounced in highly inhabited areas (urban areas), where in addition to protecting the environment, it is important to consider an anthropogenic (urban) development placed in the context of sustainable growth. This work aims at mathematically simulating the changes in the land use, by implementing an artificial neural network (ANN) model. More specifically, it will analyze how the increase of urban areas will develop and whether this development would impact on areas with particular socioeconomic and environmental value, defined as multifunctional areas. The simulation is applied to the Chianti Area, located in the province of Florence, in Italy. Chianti is an area with a unique landscape, and its territorial planning requires a careful examination of the territory in which it is inserted.

  2. Load distribution of articular cartilage from MR-images by neural nets.

    PubMed

    Seidel, Peter; Hanke, Göran; Gründer, Wilfried

    2005-01-01

    Artificial neural nets were used to determine the Young's modulus and spatial load distribution in articular cartilage by means of T2-weighted MR imaging. MR images were obtained in vitro (ex vivo?) from the joints of sheep of different ages (3 months, 9 months, 15 months, 1.5 years, 5 years, 5.5 years) and pigs (4 and 6 months) with a Bruker AMX 300 (7 T) spectrometer equipped with a micro-imaging unit. The knee of a 29-year-old male volunteer was studied in vivo under mechanical load using a clinical Siemens Vision MRT (1.5 T). The load of the cartilage is understood as a non-linear image transformation of loaded versus unloaded images. The artificial neural net was used to recognize given reference pixels of the unloaded cartilage within the image of the loaded cartilage. The Young's modulus was calculated from the local strain and the external pressure using the Hooke's law. With this method, the average Young's modulus was obtained in relationship to the biological age of the cartilage. The investigated age interval showed a progressive increase of 0.5 +/- 0.3 MPa per year. These results are consistent with published results. As shown in this pilot study, the method of neural nets allows the visualization of the spatial load distribution within the articular cartilage.

  3. D Coordinate Transformation Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Konakoglu, B.; Cakır, L.; Gökalp, E.

    2016-10-01

    Two coordinate systems used in Turkey, namely the ED50 (European Datum 1950) and ITRF96 (International Terrestrial Reference Frame 1996) coordinate systems. In most cases, it is necessary to conduct transformation from one coordinate system to another. The artificial neural network (ANN) is a new method for coordinate transformation. One of the biggest advantages of the ANN is that it can determine the relationship between two coordinate systems without a mathematical model. The aim of this study was to investigate the performances of three different ANN models (Feed Forward Back Propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Radial Basis Function Neural Network (RBFNN)) with regard to 2D coordinate transformation. To do this, three data sets were used for the same study area, the city of Trabzon. The coordinates of data sets were measured in the ED50 and ITRF96 coordinate systems by using RTK-GPS technique. Performance of each transformation method was investigated by using the coordinate differences between the known and estimated coordinates. The results showed that the ANN algorithms can be used for 2D coordinate transformation in cases where optimum model parameters are selected.

  4. A gentle introduction to artificial neural networks.

    PubMed

    Zhang, Zhongheng

    2016-10-01

    Artificial neural network (ANN) is a flexible and powerful machine learning technique. However, it is under utilized in clinical medicine because of its technical challenges. The article introduces some basic ideas behind ANN and shows how to build ANN using R in a step-by-step framework. In topology and function, ANN is in analogue to the human brain. There are input and output signals transmitting from input to output nodes. Input signals are weighted before reaching output nodes according to their respective importance. Then the combined signal is processed by activation function. I simulated a simple example to illustrate how to build a simple ANN model using nnet() function. This function allows for one hidden layer with varying number of units in that layer. The basic structure of ANN can be visualized with plug-in plot.nnet() function. The plot function is powerful that it allows for varieties of adjustment to the appearance of the neural networks. Prediction with ANN can be performed with predict() function, similar to that of conventional generalized linear models. Finally, the prediction power of ANN is examined using confusion matrix and average accuracy. It appears that ANN is slightly better than conventional linear model.

  5. Neural net diagnostics for VLSI test

    NASA Technical Reports Server (NTRS)

    Lin, T.; Tseng, H.; Wu, A.; Dogan, N.; Meador, J.

    1990-01-01

    This paper discusses the application of neural network pattern analysis algorithms to the IC fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipment. Traditional statistical techniques may be used to achieve this goal, but they can employ unrealistic a priori assumptions about measurement data. Our approach to this problem employs an adaptive pattern analysis technique based on feedforward neural networks. During training, a feedforward network automatically captures unknown sample distributions. This is important because distributions arising from the nonlinear effects of process variation can be more complex than is typically assumed. A feedforward network is also able to extract measurement features which contribute significantly to making a correct decision. Traditional feature extraction techniques employ matrix manipulations which can be particularly costly for large measurement vectors. In this paper we discuss a software system which we are developing that uses this approach. We also provide a simple example illustrating the use of the technique for fault detection in an operational amplifier.

  6. Decision net, directed graph, and neural net processing of imaging spectrometer data

    NASA Technical Reports Server (NTRS)

    Casasent, David; Liu, Shiaw-Dong; Yoneyama, Hideyuki; Barnard, Etienne

    1989-01-01

    A decision-net solution involving a novel hierarchical classifier and a set of multiple directed graphs, as well as a neural-net solution, are respectively presented for large-class problem and mixture problem treatments of imaging spectrometer data. The clustering method for hierarchical classifier design, when used with multiple directed graphs, yields an efficient decision net. New directed-graph rules for reducing local maxima as well as the number of perturbations required, and the new starting-node rules for extending the reachability and reducing the search time of the graphs, are noted to yield superior results, as indicated by an illustrative 500-class imaging spectrometer problem.

  7. [How can an otolaryngologist benefit from artificial neural networks?].

    PubMed

    Szaleniec, Joanna; Składzień, Jacek; Tadeusiewicz, Ryszard; Oleś, Krzysztof; Konior, Marcin; Przeklasa, Robert

    2012-01-01

    Artificial neural networks are informatic systems that have unique computational capabilities. The principle of their functioning is based on the rules of data processing in the brain. This article discusses the most important features of the artificial neural networks with reference to their applications in otolaryngology. The cited studies concern the fields of rhinology, audiology, phoniatrics, vestibulology, oncology, sleep apnea and salivary gland diseases. The authors also refer to their own experience with predictive neural models designed in the Department of Otolaryngology of the Jagiellonian University Medical College in Krakow. The applications of artificial neural networks in clinical diagnosis, automated signal interpretation and outcome prediction are presented. Moreover, the article explains how the artificial neural networks work and how the otolaryngologists can use them in their clinical practice and research.

  8. Artificial Neural Network applied to lightning flashes

    NASA Astrophysics Data System (ADS)

    Gin, R. B.; Guedes, D.; Bianchi, R.

    2013-05-01

    The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a

  9. 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.

  10. An introduction to bio-inspired artificial neural network architectures.

    PubMed

    Fasel, B

    2003-03-01

    In this introduction to artificial neural networks we attempt to give an overview of the most important types of neural networks employed in engineering and explain shortly how they operate and also how they relate to biological neural networks. The focus will mainly be on bio-inspired artificial neural network architectures and specifically to neo-perceptions. The latter belong to the family of convolutional neural networks. Their topology is somewhat similar to the one of the human visual cortex and they are based on receptive fields that allow, in combination with sub-sampling layers, for an improved robustness with regard to local spatial distortions. We demonstrate the application of artificial neural networks to face analysis--a domain we human beings are particularly good at, yet which poses great difficulties for digital computers running deterministic software programs.

  11. Patterns recognition of electric brain activity using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

  12. An Analysis of Stopping Criteria in Artificial Neural Networks

    DTIC Science & Technology

    1994-03-01

    I’AD-A278 491(1 AN ANALYSIS OF STOPPING CRITERIA IN ARTIFICIAL NEURAL NETWORKS THESIS Bruce Kostal Captain, USAF AFIT/GST/ENS/94M 07 D I ELECTE APR...ANALYSIS OF STOPPING CRITERIA IN ARTIFICIAL NEURAL NETWORKS THESIS Bruce Kostal Captain, USAF AFIT/GST/ENS/94M-07 ETIC ELECTE 94-12275 APR2 1994 U Approved...for public release; distributi6 unlimited D94󈧮i •6 AFIT/GST/ENS/94M-07 AN ANALYSIS OF STOPPING CRITERIA IN ARTIFICIAL NEURAL NETWORKS THESIS

  13. Forecasting Zakat collection using artificial neural network

    NASA Astrophysics Data System (ADS)

    Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina

    2013-04-01

    'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.

  14. Application of artificial neural networks to composite ply micromechanics

    NASA Technical Reports Server (NTRS)

    Brown, D. A.; Murthy, P. L. N.; Berke, L.

    1991-01-01

    Artificial neural networks can provide improved computational efficiency relative to existing methods when an algorithmic description of functional relationships is either totally unavailable or is complex in nature. For complex calculations, significant reductions in elapsed computation time are possible. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with basic information concerning the environment, constituent materials, and component ratios used in the creation of the composite. A brief introduction on neural networks is provided along with a description of the project itself.

  15. Detection of Wildfires with Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Umphlett, B.; Leeman, J.; Morrissey, M. L.

    2011-12-01

    Currently fire detection for the National Oceanic and Atmospheric Administration (NOAA) using satellite data is accomplished with algorithms and error checking human analysts. Artificial neural networks (ANNs) have been shown to be more accurate than algorithms or statistical methods for applications dealing with multiple datasets of complex observed data in the natural sciences. ANNs also deal well with multiple data sources that are not all equally reliable or equally informative to the problem. An ANN was tested to evaluate its accuracy in detecting wildfires utilizing polar orbiter numerical data from the Advanced Very High Resolution Radiometer (AVHRR). Datasets containing locations of known fires were gathered from the NOAA's polar orbiting satellites via the Comprehensive Large Array-data Stewardship System (CLASS). The data was then calibrated and navigation corrected using the Environment for Visualizing Images (ENVI). Fires were located with the aid of shapefiles generated via ArcGIS. Afterwards, several smaller ten pixel by ten pixel datasets were created for each fire (using the ENVI corrected data). Several datasets were created for each fire in order to vary fire position and avoid training the ANN to look only at fires in the center of an image. Datasets containing no fires were also created. A basic pattern recognition neural network was established with the MATLAB neural network toolbox. The datasets were then randomly separated into categories used to train, validate, and test the ANN. To prevent over fitting of the data, the mean squared error (MSE) of the network was monitored and training was stopped when the MSE began to rise. Networks were tested using each channel of the AVHRR data independently, channels 3a and 3b combined, and all six channels. The number of hidden neurons for each input set was also varied between 5-350 in steps of 5 neurons. Each configuration was run 10 times, totaling about 4,200 individual network evaluations. Thirty

  16. Geoacoustic model inversion using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Benson, Jeremy; Chapman, N. Ross; Antoniou, Andreas

    2000-12-01

    An inversion technique using artificial neural networks (ANNs) is described for estimating geoacoustic model parameters of the ocean bottom and information about the sound source from acoustic field data. The method is applied to transmission loss data from the TRIAL SABLE experiment that was carried out in shallow water off Nova Scotia. The inversion is designed to incorporate the a priori information available for the site in order to improve the estimation accuracy. The inversion scheme involves training feedforward ANNs to estimate the geoacoustic and geometric parameters using simulated input/output training pairs generated with a forward acoustic propagation model. The inputs to the ANNs are the spectral components of the transmission loss at each sensor of a vertical hydrophone array for the two lowest frequencies that were transmitted in the experiment, 35 and 55 Hz. The output is the set of environmental model parameters, both geometric and geoacoustic, corresponding to the received field. In order to decrease the training time, a separate network was trained for each parameter. The errors for the parallel estimation are 10% lower than for those obtained using a single network to estimate all the parameters simultaneously, and the training time is decreased by a factor of six. When the experimental data are presented to the ANNs the geometric parameters, such as source range and depth, are estimated with a high accuracy. Geoacoustic parameters, such as the compressional speed in the sediment and the sediment thickness, are found with a moderate accuracy.

  17. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

  18. Geophysical phenomena classification by artificial neural networks

    NASA Technical Reports Server (NTRS)

    Gough, M. P.; Bruckner, J. R.

    1995-01-01

    Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

  19. Conditional Simulation Using an Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Rizzo, D. M.; Besaw, L.

    2005-12-01

    Uncertainty in site characterization, due to sparsely distributed samples and incomplete site knowledge, is of major concern in resource mining and environmental engineering. Scientists are able to model the spatial continuity and quantify uncertainty of phenomena of interest (i.e. ore grade, subsurface contamination) through the generation and analysis of many equiprobable stochastic simulations (realizations) using concepts of probability theory. We have developed a method of generating equiprobable simulations by combining the traditional frame work of spatial dependencies witnessed in geostatistics with an artificial neural network (ANN) algorithm know as counterpropagation. This new method allows for the generation of simulations that respect the observed sample data as well as the data's underlying spatial structure. Conditional simulation is a natural product of the counterpropagation network using random initial weights while its architecture has computational advantages over other simulation generators due to its parallel information passing topology. Computational speedup, due to the implementation of the algorithm on a local cluster of off-the-shelf computational nodes and software, is another factor that will be discussed. The results of this research illustrate the potential applicability and utility of using the counterpropagation algorithm to conduct a probabilistic assessment while increasing interpretational value of site characterization data.

  20. Groundwater remediation optimization using artificial neural networks

    SciTech Connect

    Rogers, L. L., LLNL

    1998-05-01

    One continuing point of research in optimizing groundwater quality management is reduction of computational burden which is particularly limiting in field-scale applications. Often evaluation of a single pumping strategy, i.e. one call to the groundwater flow and transport model (GFTM) may take several hours on a reasonably fast workstation. For computational flexibility and efficiency, optimal groundwater remediation design at Lawrence Livermore National Laboratory (LLNL) has relied on artificial neural networks (ANNS) trained to approximate the outcome of 2-D field-scale, finite difference/finite element GFTMs. The search itself has been directed primarily by the genetic algorithm (GA) or the simulated annealing (SA) algorithm. This approach has advantages of (1) up to a million fold increase in speed of remediation pattern assessment during the searches and sensitivity analyses for the 2-D LLNL work, (2) freedom from sequential runs of the GFTM (enables workstation farming), and (3) recycling of the knowledge base (i.e. runs of the GFTM necessary to train the ANNS). Reviewed here are the background and motivation for such work, recent applications, and continuing issues of research.

  1. Geophysical phenomena classification by artificial neural networks

    NASA Technical Reports Server (NTRS)

    Gough, M. P.; Bruckner, J. R.

    1995-01-01

    Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

  2. Neural net learning issues in classification of free text documents

    SciTech Connect

    Dasigi, V.R.; Mann, R.C.

    1996-03-01

    In intelligent analysis of large amounts of text, not any single clue indicates reliably that a pattern of interest has been found. When using multiple clues, it is not known how these should be integrated into a decision. In the context of this investigation, we have been using neural nets as parameterized mappings that allow for fusion of higher level clues extracted from free text. By using higher level clues and features, we avoid very large networks. By using the dominant singular values computed by Latent Semantic Indexing (LSI) and applying neural network algorithms for integrating these values and the outputs from other ``sensors,`` we have obtained preliminary encouraging results with text classification.

  3. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  4. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  5. Opposition logic and neural network models in artificial grammar learning.

    PubMed

    Vokey, John R; Higham, Philip A

    2004-09-01

    Following neural network simulations of the two experiments of, argued that the opposition logic advocated by was incapable of distinguishing between single and multiple influences on performance of artificial grammar learning and more generally. We show that their simulations do not support their conclusions. We also provide different neural network simulations that do simulate the essential results of Higham et al. (2000).

  6. Multiple image sensor data fusion through artificial neural networks

    USDA-ARS?s Scientific Manuscript database

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  7. Using Artificial Neural Networks to Predict Malignancy of Ovarian Tumors

    DTIC Science & Technology

    2007-11-02

    This paper discusses the application of artificial neural networks (ANNs) to preoperative discrimination between benign and malignant ovarian tumors...With the input variables selected by logistic regression analysis, two types of feed-forward neural networks were built: multi-layer perceptrons

  8. Advances in Artificial Neural Networks - Methodological Development and Application

    USDA-ARS?s Scientific Manuscript database

    Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

  9. A real time neural net estimator of fatigue life

    NASA Technical Reports Server (NTRS)

    Troudet, T.; Merrill, W.

    1990-01-01

    A neural network architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopumps blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high-frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines.

  10. A real time neural net estimator of fatigue life

    NASA Technical Reports Server (NTRS)

    Troudet, T.; Merrill, W.

    1990-01-01

    A neural network architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopumps blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high-frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines.

  11. Prediction of Buffet Loads Using Artificial Neural Networks

    DTIC Science & Technology

    2001-09-01

    The use of artificial neural networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The...with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural ... networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Based on the ANN methodology investigated, a

  12. Are artificial neural networks white boxes?

    PubMed

    Kolman, Eyal; Margaliot, Michael

    2005-07-01

    In this paper, we introduce a novel Mamdani-type fuzzy model, referred to as the all-permutations fuzzy rule base (APFRB), and show that it is mathematically equivalent to a standard feedforward neural network. We describe several applications of this equivalence between a neural network and our fuzzy rule base (FRB), including knowledge extraction from and knowledge insertion into neural networks.

  13. An artificial neural network based groundwater flow and transport simulator

    SciTech Connect

    Krom, T.D.; Rosbjerg, D.

    1998-07-01

    Artificial neural networks are investigated as a tool for the simulation of contaminant loss and recovery in three-dimensional heterogeneous groundwater flow and contaminant transport modeling. These methods have useful applications in expert system development, knowledge base development and optimization of groundwater pollution remediation. The numerical model runs used to develop the artificial neural networks can be re-used to develop artificial neural networks to address alternative optimization problems or changed formulations of the constraints and or objective function under optimization. Artificial neural networks have been analyzed with the goal of estimating objectives which normally require the use of traditional flow and transport codes: such as contaminant recovery, contaminant loss (unrecovered) and remediation failure. The inputs to the artificial neutral networks are variable pumping withdrawal rates at fairly unconstrained 3-D locations. A forward-feed backwards error propagation artificial neural network architecture is used. The significance of the size of the training set, network architecture, and network weight optimization algorithm with respect to the estimation accuracy and objective are shown to be important. Finally, the quality of the weight optimization is studied via cross-validation techniques. This is demonstrated to be a useful method for judging training performance for strongly under-described systems.

  14. Prediction of vascular tissue engineering results with artificial neural networks.

    PubMed

    Xu, Jie; Ge, Haiyan; Zhou, Xiaolin; Yan, Jinglong; Chi, Qiang; Zhang, Zhipeng

    2005-12-01

    Tissue engineers are often confused on finding the most successful strategy for specific patient. In this study, we used artificial neural networks to predict the outcomes of different vascular tissue engineering strategies, thus providing advisory information for experimental designers. Over 30 variables were used as features of the tissue engineering strategies. Different architectures of artificial neural networks with back propagation algorithm were tested to obtain the best model configuration for the prediction of the tissue engineering strategies. In the computational experiments, the artificial neural networks with one and two hidden layers could, respectively, detect unsuccessful strategies with the highest predictive accuracy of 91.45 and 94.24%. In conclusion, artificial intelligence has great potential in tissue engineering decision support. It can provide accurate advisory information for tissue engineers, thus reducing failures and improving therapeutic effects.

  15. Optoelectronic analogs of self-programming neural nets - Architecture and methodologies for implementing fast stochastic learning by simulated annealing

    NASA Technical Reports Server (NTRS)

    Farhat, Nabil H.

    1987-01-01

    Self-organization and learning is a distinctive feature of neural nets and processors that sets them apart from conventional approaches to signal processing. It leads to self-programmability which alleviates the problem of programming complexity in artificial neural nets. In this paper architectures for partitioning an optoelectronic analog of a neural net into distinct layers with prescribed interconnectivity pattern to enable stochastic learning by simulated annealing in the context of a Boltzmann machine are presented. Stochastic learning is of interest because of its relevance to the role of noise in biological neural nets. Practical considerations and methodologies for appreciably accelerating stochastic learning in such a multilayered net are described. These include the use of parallel optical computing of the global energy of the net, the use of fast nonvolatile programmable spatial light modulators to realize fast plasticity, optical generation of random number arrays, and an adaptive noisy thresholding scheme that also makes stochastic learning more biologically plausible. The findings reported predict optoelectronic chips that can be used in the realization of optical learning machines.

  16. Optoelectronic analogs of self-programming neural nets - Architecture and methodologies for implementing fast stochastic learning by simulated annealing

    NASA Technical Reports Server (NTRS)

    Farhat, Nabil H.

    1987-01-01

    Self-organization and learning is a distinctive feature of neural nets and processors that sets them apart from conventional approaches to signal processing. It leads to self-programmability which alleviates the problem of programming complexity in artificial neural nets. In this paper architectures for partitioning an optoelectronic analog of a neural net into distinct layers with prescribed interconnectivity pattern to enable stochastic learning by simulated annealing in the context of a Boltzmann machine are presented. Stochastic learning is of interest because of its relevance to the role of noise in biological neural nets. Practical considerations and methodologies for appreciably accelerating stochastic learning in such a multilayered net are described. These include the use of parallel optical computing of the global energy of the net, the use of fast nonvolatile programmable spatial light modulators to realize fast plasticity, optical generation of random number arrays, and an adaptive noisy thresholding scheme that also makes stochastic learning more biologically plausible. The findings reported predict optoelectronic chips that can be used in the realization of optical learning machines.

  17. Optoelectronic analogs of self-programming neural nets: architecture and methodologies for implementing fast stochastic learning by simulated annealing.

    PubMed

    Farhat, N H

    1987-12-01

    Self-organization and learning is a distinctive feature of neural nets and processors that sets them apart from conventional approaches to signal processing. It leads to self-programmability which alleviates the problem of programming complexity in artificial neural nets. In this paper architectures for partitioning an optoelectronic analog of a neural net into distinct layers with prescribed interconnectivity pattern to enable stochastic learning by simulated annealing in the context of a Boltzmann machine are presented. Stochastic learning is of interest because of its relevance to the role of noise in biological neural nets. Practical considerations and methodologies for appreciably accelerating stochastic learning in such a multilayered net are described. These include the use of parallel optical computing of the global energy of the net, the use of fast nonvolatile programmable spatial light modulators to realize fast plasticity, optical generation of random number arrays, and an adaptive noisy thresholding scheme that also makes stochastic learning more biologically plausible. The findings reported predict optoelectronic chips that can be used in the realization of optical learning machines.

  18. Automatic voice recognition using traditional and artificial neural network approaches

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1989-01-01

    The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

  19. Automatic labeling and characterization of objects using artificial neural networks

    NASA Technical Reports Server (NTRS)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  20. Automatic labeling and characterization of objects using artificial neural networks

    NASA Technical Reports Server (NTRS)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  1. Toward implementation of artificial neural networks that "really work".

    PubMed Central

    Leon, M. A.; Keller, J.

    1997-01-01

    Artificial neural networks are established analytical methods in bio-medical research. They have repeatedly outperformed traditional tools for pattern recognition and clinical outcome prediction while assuring continued adaptation and learning. However, successful experimental neural networks systems seldom reach a production state. That is, they are not incorporated into clinical information systems. It could be speculated that neural networks simply must undergo a lengthy acceptance process before they become part of the day to day operations of health care systems. However, our experience trying to incorporate experimental neural networks into information systems lead us to believe that there are technical and operational barriers that greatly difficult neural network implementation. A solution for these problems may be the delineation of policies and procedures for neural network implementation and the development a new class of neural network client/server applications that fit the needs of current clinical information systems. PMID:9357613

  2. Face recognition: Eigenface, elastic matching, and neural nets

    SciTech Connect

    Zhang, J.; Yan, Y.; Lades, M.

    1997-09-01

    This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice.

  3. Medical image analysis with artificial neural networks.

    PubMed

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  4. Adaptive Neurons For Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  5. Contact prediction using mutual information and neural nets.

    PubMed

    Shackelford, George; Karplus, Kevin

    2007-01-01

    Prediction of protein structures continues to be a difficult problem, particularly when there are no solved structures for homologous proteins to use as templates. Local structure prediction (secondary structure and burial) is fairly reliable, but does not provide enough information to produce complete three-dimensional structures. Residue-residue contact prediction, though still not highly reliable, may provide a useful guide for assembling local structure prediction into full tertiary prediction. We develop a neural network which is applied to pairs of residue positions and outputs a probability of contact between the positions. One of the neural net inputs is a novel statistic for detecting correlated mutations: the statistical significance of the mutual information between the corresponding columns of a multiple sequence alignment. This statistic, combined with a second statistic based on the propensity of two amino acid types being in contact, results in a simple neural network that is a good predictor of contacts. Adding more features from amino-acid distributions and local structure predictions, the final neural network predicts contacts better than other submitted contact predictions at CASP7, including contact predictions derived from fragment-based tertiary models on free-modeling domains. It is still not known if contact predictions can improve tertiary models on free-modeling domains. Available at http://www.soe.ucsc.edu/research/compbio/SAM_T06/T06-query.html. (c) 2007 Wiley-Liss, Inc.

  6. Artificial neural networks reveal efficiency in genetic value prediction.

    PubMed

    Peixoto, L A; Bhering, L L; Cruz, C D

    2015-06-18

    The objective of this study was to evaluate the efficiency of artificial neural networks (ANNs) for predicting genetic value in experiments carried out in randomized blocks. Sixteen scenarios were simulated with different values of heritability (10, 20, 30, and 40%), coefficient of variation (5 and 10%), and the number of genotypes per block (150 and 200 for validation, and 5000 for neural network training). One hundred validation populations were used in each scenario. Accuracy of ANNs was evaluated by comparing the correlation of network value with genetic value, and of phenotypic value with genetic value. Neural networks were efficient in predicting genetic value with a 0.64 to 10.3% gain compared to the phenotypic value, regardless the simulated population size, heritability, or coefficient of variation. Thus, the artificial neural network is a promising technique for predicting genetic value in balanced experiments.

  7. Functional approximation using artificial neural networks in structural mechanics

    NASA Technical Reports Server (NTRS)

    Alam, Javed; Berke, Laszlo

    1993-01-01

    The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed.

  8. Simulation of nonlinear random vibrations using artificial neural networks

    SciTech Connect

    Paez, T.L.; Tucker, S.; O`Gorman, C.

    1997-02-01

    The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.

  9. [Working Temperature Predication of Artificial Heart Based on Neural Network].

    PubMed

    Li, Qilei; Yang, Ming; Ou, Wenchu; Meng, Fan; Xu, Zihao; Xu, Liang

    2015-03-01

    The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as well as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetically optimized BP network, by which indicates that the probability is 1.84% with the margin of error more than 1%.

  10. Application of artificial neural networks to gaming

    NASA Astrophysics Data System (ADS)

    Baba, Norio; Kita, Tomio; Oda, Kazuhiro

    1995-04-01

    Recently, neural network technology has been applied to various actual problems. It has succeeded in producing a large number of intelligent systems. In this article, we suggest that it could be applied to the field of gaming. In particular, we suggest that the neural network model could be used to mimic players' characters. Several computer simulation results using a computer gaming system which is a modified version of the COMMONS GAME confirm our idea.

  11. Automated Defect Classification Using AN Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Chady, T.; Caryk, M.; Piekarczyk, B.

    2009-03-01

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  12. AUTOMATED DEFECT CLASSIFICATION USING AN ARTIFICIAL NEURAL NETWORK

    SciTech Connect

    Chady, T.; Caryk, M.; Piekarczyk, B.

    2009-03-03

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  13. Neural net robot controller with guaranteed tracking performance.

    PubMed

    Lewis, F L; Liu, K; Yesildirek, A

    1995-01-01

    A neural net (NN) controller for a general serial-link robot arm is developed. The NN has two layers so that linearity in the parameters holds, but the "net functional reconstruction error" and robot disturbance input are taken as nonzero. The structure of the NN controller is derived using a filtered error/passivity approach, leading to new NN passivity properties. Online weight tuning algorithms including a correction term to backpropagation, plus an added robustifying signal, guarantee tracking as well as bounded NN weights. The NN controller structure has an outer tracking loop so that the NN weights are conveniently initialized at zero, with learning occurring online in real-time. It is shown that standard backpropagation, when used for real-time closed-loop control, can yield unbounded NN weights if (1) the net cannot exactly reconstruct a certain required control function or (2) there are bounded unknown disturbances in the robot dynamics. The role of persistency of excitation is explored.

  14. Artificial neural networks for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Daniell, Cindy E.; Kemsley, David; Lincoln, William P.; Tackett, Walter A.; Baraghimian, Gregory A.

    1992-12-01

    The Self Adaptive Hierarchical Target Identification and Recognition Neural Network (SAHTIRNTM), is a unique and powerful combination of state-of-the-art neural network models for automatic target recognition applications. It is a combination of three models: (1) an early vision segmentor based on the Canny edge detector, (2) a hierarchical feature extraction and pattern recognition system based on a modified Neocognitron architecture, and (3) a pattern classifier based on the back-propagation network. Hughes has extensively tested SAHTIRNTM with several ground vehicular targets using terrain board modeled IR imagery under a current neural network program sponsored by the Defense Advanced Research Projects Agency. In addition, extensive testing was conducted using several real IR and handwritten character databases. Hughes has demonstrated successful performance with 91 to 100% probability of correct classification over this wide variety of data. End-to-end system results from these experiments are provided and interim results from each stage of the SAHTIRNTM system are discussed.

  15. Artificial neural networks: theoretical background and pharmaceutical applications: a review.

    PubMed

    Wesolowski, Marek; Suchacz, Bogdan

    2012-01-01

    In recent times, there has been a growing interest in artificial neural networks, which are a rough simulation of the information processing ability of the human brain, as modern and vastly sophisticated computational techniques. This interest has also been reflected in the pharmaceutical sciences. This paper presents a review of articles on the subject of the application of neural networks as effective tools assisting the solution of various problems in science and the pharmaceutical industry, especially those characterized by multivariate and nonlinear dependencies. After a short description of theoretical background and practical basics concerning the computations performed by means of neural networks, the most important pharmaceutical applications of neural networks, with suitable references, are demonstrated. The huge role played by neural networks in pharmaceutical analysis, pharmaceutical technology, and searching for the relationships between the chemical structure and the properties of newly synthesized compounds as candidates for drugs is discussed.

  16. SU-F-BRD-11: Prediction of Dosimetric Endpoints From Patient Geometry Using Neural Nets

    SciTech Connect

    O'Connell, D; Chow, P; Agazaryan, N; Jani, S; Low, D; Lamb, J

    2014-06-15

    Purpose: The previously-published overlap volume histogram (OVH) technique lends itself naturally to prediction of the dose received by a given volume of tissue (e.g. D90) in intensity-modulated radiotherapy (IMRT) treatment plans. Here we extend the OVH technique using artificial neural networks in order to predict the volume of tissue receiving a given dose (e.g. V90) in both prostate IMRT and conventional breast radiotherapy. Methods: Twenty-nine prostate treatment plans and forty-three breast treatment plans were analyzed. The spatial relationships between the prostate and rectum and between the breast and ipsilateral lung were characterized using OVHs. The OVH is a cumulative histogram representing the fractional volume of the risk organ overlapped by a series of isotropic expansions of the planning target volume (PTV). Seven cases were identified as outliers and replanned. OVH points were used as inputs to a one hidden layer feed forward artificial neural network with quality parameters of the corresponding plan, such as the rectum V50, as targets. A 3-fold cross-validation was used to estimate the prediction error. Results: The root mean square (RMS) error between the predicted rectum V50s and the planned values was 2.3, which was 35% of the standard deviation of V50 for the twenty-nine plans. The RMS error of prediction of V20 of the ipsilateral lung in breast cases was 3.9, which was 90% of the standard deviation of the V20 values in the breast plan database. Conclusion: This study demonstrates that artificial neural nets can be used to extend the OVH technique to predict dosimetric endpoints taking the form of a volume receiving a given dose, rather than the minimum dose received by a given volume. Prediction of ipsilateral lung dose in breast radiotherapy using the OVH technique remains a work in progress.

  17. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    ERIC Educational Resources Information Center

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  18. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    ERIC Educational Resources Information Center

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  19. Artificial Neural Networks in Policy Research: A Current Assessment.

    ERIC Educational Resources Information Center

    Woelfel, Joseph

    1993-01-01

    Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

  20. Introducing Artificial Neural Networks through a Spreadsheet Model

    ERIC Educational Resources Information Center

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  1. Prediction of bioactive peptides using artificial neural networks.

    PubMed

    Andreu, David; Torrent, Marc

    2015-01-01

    Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.

  2. Introducing Artificial Neural Networks through a Spreadsheet Model

    ERIC Educational Resources Information Center

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  3. Artificial Neural Networks in Policy Research: A Current Assessment.

    ERIC Educational Resources Information Center

    Woelfel, Joseph

    1993-01-01

    Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

  4. Artificial-neural-network-based failure detection and isolation

    NASA Astrophysics Data System (ADS)

    Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

    1998-03-01

    This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

  5. [Artificial neural networks for decision making in urologic oncology].

    PubMed

    Remzi, M; Djavan, B

    2007-06-01

    This chapter presents a detailed introduction regarding Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. It includes a description of ANNs methodology and points out the differences between Artifical Intelligence and traditional statistic models in terms of usefulness for patients and clinicians, and its advantages over current statistical analysis.

  6. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    ERIC Educational Resources Information Center

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  7. Use of artificial neural networks as estimators and controllers

    NASA Astrophysics Data System (ADS)

    Concilio, Antonio; Sorrentino, A.

    1996-04-01

    Active noise control is one among the most promising applications of the so-called Smart Structures, because it ensures, or promises, lower weight, lower cost, more effectiveness and all what is desirable in a vehicle design process, with respect to the current solutions. More and more attention in the research world has been devoting to this argument, pushed by both political, economical and environmental reasons, the one connected to the others. Piezoceramic actuators, integrated into the structure, seem to offer the most fashionable and practical solutions among all the proposed architectures, [1-2]. As sensors, microphones demonstrated to be the most performing, above all because they give the most suitable representation of the field that has to be cancelled, [3-4]. This approach is known as Acousto-Structural Active Control, ASAC, [5]. However, according to Fuller's definition, [6] , an intelligent controller is needed to ensure the development of an "Intelligent Structure" . Its main characteristic should be represented by the capability of learning by examples, of following the structure during its evolution, of being the system "brain" . This peculiarity may be offered by Artificial Neural Networks (ANN's), [7-8]. They present other important features, like the capability, in principle, of treating non-linear as well as linear problems, [9], of identifying dynamic systems, [10], of properly acting as a controller. Then, such a net could integrate in itself the function of "system estimator" or "observer" ,and of interpolator - extrapolator and controller, contemporarily. The authors have been working on such subjects for a long time, proposing for instance ANN's as time-domain structural parameters estimators on a simple 2D element ( a framed plate), [11], as noise and vibration controllers in a FF system, [12-13], as materials damping parameters extractors from experimental data, [14]. All these applications were aimed at noise reduction problems. The

  8. 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.

  9. Stability Training for Convolutional Neural Nets in LArTPC

    NASA Astrophysics Data System (ADS)

    Lindsay, Matt; Wongjirad, Taritree

    2017-01-01

    Convolutional Neural Nets (CNNs) are the state of the art for many problems in computer vision and are a promising method for classifying interactions in Liquid Argon Time Projection Chambers (LArTPCs) used in neutrino oscillation experiments. Despite the good performance of CNN's, they are not without drawbacks, chief among them is vulnerability to noise and small perturbations to the input. One solution to this problem is a modification to the learning process called Stability Training developed by Zheng et al. We verify existing work and demonstrate volatility caused by simple Gaussian noise and also that the volatility can be nearly eliminated with Stability Training. We then go further and show that a traditional CNN is also vulnerable to realistic experimental noise and that a stability trained CNN remains accurate despite noise. This further adds to the optimism for CNNs for work in LArTPCs and other applications.

  10. Artificial neural networks for decision-making in urologic oncology.

    PubMed

    Anagnostou, Theodore; Remzi, Mesut; Lykourinas, Michael; Djavan, Bob

    2003-06-01

    The authors are presenting a thorough introduction in Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. The article covers a description of Artificial Neural Network methodology and points out the differences of Artificial Intelligence to traditional statistic models in terms of serving patients and clinicians, in a different way than current statistical analysis. Since Artificial Intelligence is not yet fully understood by many practicing clinicians, the authors have reviewed a careful selection of articles in order to explore the clinical benefit of Artificial Intelligence applications in modern Urology questions and decision-making. The data are from real patients and reflect attempts to achieve more accurate diagnosis and prognosis, especially in prostate cancer that stands as a good example of difficult decision-making in everyday practice. Experience from current use of Artificial Intelligence is also being discussed, and the authors address future developments as well as potential problems such as medical record quality, precautions in using ANNs or resistance to system use, in an attempt to point out future demands and the need for common standards. The authors conclude that both methods should continue to be used in a complementary manner. ANNs still do not prove always better as to replace standard statistical analysis as the method of choice in interpreting medical data.

  11. A neutron spectrum unfolding computer code based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2014-02-01

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

  12. Evaluating neural networks and artificial intelligence systems

    NASA Astrophysics Data System (ADS)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  13. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1989-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.

  14. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1989-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.

  15. Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements.

    PubMed

    Ferreira, Monique S; Galo, Maria De Lourdes B T

    2013-01-01

    Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.

  16. Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

    NASA Technical Reports Server (NTRS)

    Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

    1993-01-01

    A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular

  17. Artificial neural networks as tool for atmospheric modeling and prediction

    NASA Astrophysics Data System (ADS)

    Scharringhausen, Marco

    Applications of the theory of neural networks to the modeling of the middle and upper atmo-sphere are presented. Artificial neural networks are a tool to mimic biological behavior and exploit capabilities in terms of learning and pattern recognition. Neural networks belong to the class of "first-principles" models. That is, the interaction between individual neuron cells is modeled. The basic tuning parameter in such networks is the set of weights describing the interconnections between individual neuron cells. by adjustment of the weight vector, the network can be trained to reproduce a certain amount of data. The MSIS-2000 model is utilized to train an artificial neural network. Certain atmospheric state parameters such as temperature, ion density are included in the analysis. It is shown that after a training phase covering the years 1970 -1995 the neural network reproduces the model calculations of 1996 -2005 to a good extend. The deviations are quantified. It is concluded that after a training phase the neural network can be utilized to predict the atmospheric state beyond the actual time phase covered by the empirical model. This method is suggested to be utilized for different kinds of empirical models, e.g. models of solar variability.

  18. Neural-Net Based Optical NDE Method for Structural Health Monitoring

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Weiland, Kenneth E.

    2003-01-01

    This paper answers some performance and calibration questions about a non-destructive-evaluation (NDE) procedure that uses artificial neural networks to detect structural damage or other changes from sub-sampled characteristic patterns. The method shows increasing sensitivity as the number of sub-samples increases from 108 to 6912. The sensitivity of this robust NDE method is not affected by noisy excitations of the first vibration mode. A calibration procedure is proposed and demonstrated where the output of a trained net can be correlated with the outputs of the point sensors used for vibration testing. The calibration procedure is based on controlled changes of fastener torques. A heterodyne interferometer is used as a displacement sensor for a demonstration of the challenges to be handled in using standard point sensors for calibration.

  19. Design of speaker recognition system based on artificial neural network

    NASA Astrophysics Data System (ADS)

    Chen, Yanhong; Wang, Li; Lin, Han; Li, Jinlong

    2012-10-01

    Speaker recognition is to recognize speaker's identity from its voice which contains physiological and behavioral characteristics unique to each individual. In this paper, the artificial neural network model, which has very good capacity of non-linear division in characteristic space, is used for pattern matching. The speaker's sample characteristic domain is built for his mixed voice characteristic signals based on Kmeanlbg algorithm. Then the dimension of the inputting eigenvector is reduced, and the redundant information is got rid of. On this basis, BP neural network is used to divide capacity area for characteristic space nonlinearly, and the BP neural network acts as a classifier for the speaker. Finally, a speaker recognition system based on the neural network is realized and the experiment results validate the recognition performance and robustness of the system.

  20. Lexical tone recognition with an artificial neural network.

    PubMed

    Zhou, Ning; Zhang, Wenle; Lee, Chao-Yang; Xu, Li

    2008-06-01

    Tone production is particularly important for communicating in tone languages such as Mandarin Chinese. In the present study, an artificial neural network was used to recognize tones produced by adult native speakers. The purposes of the study were (1) to test the sensitivity of the neural network to speaker variation typically in adult speaker groups, (2) to evaluate two normalization procedures to overcome the effects of speaker variation, and (3) to compare tone recognition performance of the neural network with that of the human listeners. A feedforward multilayer neural network was used. Twenty-nine adult native Mandarin Chinese speakers were recruited to record tone samples. The F0 contours of the vowel part of the 1044 monosyllabic words recorded were extracted using an autocorrelation method. Samples from the F0 contours were used as inputs to the neural network. The efficacy of the neural network was first tested by varying the number of inputs and the number of neurons in the hidden layer from 1 to 16. The sensitivity of the neural network to speaker variation was tested by (1) using the raw F0 data from speech tokens of a number of randomly drawn speakers that varied from 1 to 29, (2) using the raw F0 data from speech tokens of either male-only or female-only speakers, and (3) using two sets of normalized F0 data (i.e., tone 1-based normalization and first-order derivative) from speech tokens from a number of randomly drawn speakers that varied from 1 to 29. The recognition performance of the neural network under several experimental conditions was compared with the corresponding recognition performance of 10 normal-hearing, native Mandarin Chinese speaking adult listeners. Three inputs and four hidden neurons were found to be sufficient for the neural network to perform at about 85% correct using speech samples without normalization. The performance of the neural network was affected by variation across speakers particularly between genders. Using the

  1. Unsupervised classification of neural spikes with a hybrid multilayer artificial neural network.

    PubMed

    García, P; Suárez, C P; Rodríguez, J; Rodríguez, M

    1998-07-01

    The understanding of the brain structure and function and its computational style is one of the biggest challenges both in Neuroscience and Neural Computation. In order to reach this and to test the predictions of neural network modeling, it is necessary to observe the activity of neural populations. In this paper we propose a hybrid modular computational system for the spike classification of multiunits recordings. It works with no knowledge about the waveform, and it consists of two moduli: a Preprocessing (Segmentation) module, which performs the detection and centering of spike vectors using programmed computation; and a Processing (Classification) module, which implements the general approach of neural classification: feature extraction, clustering and discrimination, by means of a hybrid unsupervised multilayer artificial neural network (HUMANN). The operations of this artificial neural network on the spike vectors are: (i) compression with a Sanger Layer from 70 points vector to five principal component vector; (ii) their waveform is analyzed by a Kohonen layer; (iii) the electrical noise and overlapping spikes are rejected by a previously unreported artificial neural network named Tolerance layer; and (iv) finally the spikes are labeled into spike classes by a Labeling layer. Each layer of the system has a specific unsupervised learning rule that progressively modifies itself until the performance of the layer has been automatically optimized. The procedure showed a high sensitivity and specificity also when working with signals containing four spike types.

  2. Artificial Kohonen's neural networks for computer capillarometry

    NASA Astrophysics Data System (ADS)

    Doncow, Sergey; Orbachevskyi, Leonid; Birukow, Valentin; Stepanova, Nina V.

    1998-04-01

    Analysis of disorders in microhemocirculation appearing during development of various genesis pathological processes allows to manifest their start-up and pathogenic mechanisms. Numerical estimation of capillary and arteriolar-veinular bed parameters gives a possibility to predict development of these states. Automatically controlled estimation of even simple parameters of microhemocirculation bed, such as vessel length in the eye conjuctiva, their amount, size, winding, etc., allows to speak about a new method in the express diagnostics of computer capillarometry. The present work is devoted to the methods of automated determination of microvessel length. The problem is solved by application of one-dimension Kohonen's networks for adaptive uniform piecewise approximation of capillary images. Kohonen's units are the approximation points, and their position is set during the self-organization of the neural network.

  3. Probability density estimation using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Likas, Aristidis

    2001-04-01

    We present an approach for the estimation of probability density functions (pdf) given a set of observations. It is based on the use of feedforward multilayer neural networks with sigmoid hidden units. The particular characteristic of the method is that the output of the network is not a pdf, therefore, the computation of the network's integral is required. When this integral cannot be performed analytically, one is forced to resort to numerical integration techniques. It turns out that this is quite tricky when coupled with subsequent training procedures. Several modifications of the original approach (Modha and Fainman, 1994) are proposed, most of them related to the numerical treatment of the integral and the employment of a preprocessing phase where the network parameters are initialized using supervised training. Experimental results using several test problems indicate that the proposed method is very effective and in most cases superior to the method of Gaussian mixtures.

  4. Microwave nonlinear device modelling by using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Li, Xiuping; Gao, Jianjun; Boeck, Georg

    2006-07-01

    An approach for the microwave nonlinear device modelling technique based on a combination of the conventional equivalent circuit model and artificial neural network (ANN) is presented in this paper. The main advantage of the proposed method is that the integration and differential of an ANN can directly be carried out from the original ANN. The proposed technique is very useful for neural-based microwave computer-aided design, and for analytically unified dc, small signal and nonlinear device modelling. Examples of the Schottky diode and PHEMT linear/nonlinear modelling utilizing the proposed integration and differential technique are demonstrated.

  5. Neural-net based coordinated stabilizing control for the exciter and governor loops of low head hydropower plants

    SciTech Connect

    Djukanovic, M.; Novicevic, M.; Dobrijevic, D.; Babic, B.; Sobajic, D.J.; Pao, Y.H. |

    1995-12-01

    This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-space optimal control and neural-net based control are presented. Results obtained on the non-linear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.

  6. 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.

  7. Numerical solution of differential equations by artificial neural networks

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J., Jr.

    1995-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  8. Adaptive evolutionary artificial neural networks for pattern classification.

    PubMed

    Oong, Tatt Hee; Isa, Nor Ashidi Mat

    2011-11-01

    This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms.

  9. Mechatronic Hydraulic Drive with Regulator, Based on Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Burennikov, Y.; Kozlov, L.; Pyliavets, V.; Piontkevych, O.

    2017-06-01

    Mechatronic hydraulic drives, based on variable pump, proportional hydraulics and controllers find wide application in technological machines and testing equipment. Mechatronic hydraulic drives provide necessary parameters of actuating elements motion with the possibility of their correction in case of external loads change. This enables to improve the quality of working operations, increase the capacity of machines. The scheme of mechatronic hydraulic drive, based on the pump, hydraulic cylinder, proportional valve with electrohydraulic control and programmable controller is suggested. Algorithm for the control of mechatronic hydraulic drive to provide necessary pressure change law in hydraulic cylinder is developed. For the realization of control algorithm in the controller artificial neural networks are used. Mathematical model of mechatronic hydraulic drive, enabling to create the training base for adjustment of artificial neural networks of the regulator is developed.

  10. Artificial neural network modeling of p-cresol photodegradation

    PubMed Central

    2013-01-01

    Background The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. Results The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. Conclusion Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study. PMID:23731706

  11. Using Artificial Neural Networks to Assess Changes in Microbial Communities

    SciTech Connect

    Brandt, C.C.; Macnaughton, S.; Palumbo, A.V.; Pfiffner, S.M.; Schryver, J.C.

    1999-04-19

    We evaluated artificial neural networks (ANNs) as a technique for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker (SLB) data collected from two soil microcosm experiments using traditional statistical techniques and ANN. Two phases of data analysis were done; pattern recognition and prediction. In general, the ANNs were better able to detect patterns and relationships in the SLB data than were the traditional statistical techniques.

  12. Unique Applications for Artificial Neural Networks. Phase 1

    DTIC Science & Technology

    1991-08-08

    practical decision making problems. Application includes retail distribution, school bus routing, mail and newspaper delivery, municipal waste collection ...Contents Unique Applications for Artificial Neural Networks Acknowledgments Table of Contents Abstract i 1.0 Introduction 1 2.0 The NGO- VRP Solver 2...including: i) the generic vehicle routing problem ( VRP ), in which there is a fleet of vehicles with capacities, and stops have demand levels for

  13. Automated Online Data Analysis by Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Singh, Harinder P.; Gupta, Ranjan

    Artificial neural networks have been successfully used recently for automated stellar spectral classification of the UV visible and the IR spectral databases. We propose to build an ANN based online data analysis pipeline in collaboration with VO India for the new INDO-US stellar spectral library of nearly 1300 stars from 3500-9500Å observed from the KPNO Coude feed by Valdes et al. (2003). The paper discusses important ingredients of such a pipeline.

  14. Time series prediction using artificial neural network for power stabilization

    SciTech Connect

    Puranik, G.; Philip, T.; Nail, B.

    1996-12-31

    Time series prediction has been applied to many business and scientific applications. Prominent among them are stock market prediction, weather forecasting, etc. Here, this technique has been applied to forecast plasma torch voltages to stabilize power using a backpropagation, a model of artificial neural network. The Extended-Delta-Bar-Delta algorithm is used to improve the convergence rate of the network and also to avoid local minima. Results from off-line data was quite promising to use in on-line.

  15. Electricity price short-term forecasting using artificial neural networks

    SciTech Connect

    Szkuta, B.R.; Sanabria, L.A.; Dillon, T.S.

    1999-08-01

    This paper presents the System Marginal Price (SMP) short-term forecasting implementation using the Artificial Neural Networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with back-propagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP.

  16. Artificial neural network modeling of p-cresol photodegradation.

    PubMed

    Abdollahi, Yadollah; Zakaria, Azmi; Abbasiyannejad, Mina; Masoumi, Hamid Reza Fard; Moghaddam, Mansour Ghaffari; Matori, Khamirul Amin; Jahangirian, Hossein; Keshavarzi, Ashkan

    2013-06-03

    The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study.

  17. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

  18. Classifying auroras using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Rydesater, Peter; Brandstrom, Urban; Steen, Ake; Gustavsson, Bjorn

    1999-03-01

    In Auroral Large Imaging System (ALIS) there is need of stable methods for analysis and classification of auroral images and images with for example mother of pearl clouds. This part of ALIS is called Selective Imaging Techniques (SIT) and is intended to sort out images of scientific interest. It's also used to find out what and where in the images there is for example different auroral phenomena's. We will discuss some about the SIT units main functionality but this work is mainly concentrated on how to find auroral arcs and how they are placed in images. Special case have been taken to make the algorithm robust since it's going to be implemented in a SIT unit which will work automatic and often unsupervised and some extends control the data taking of ALIS. The method for finding auroral arcs is based on a local operator that detects intensity differens. This gives arc orientation values as a preprocessing which is fed to a neural network classifier. We will show some preliminary results and possibilities to use and improve this algorithm for use in the future SIT unit.

  19. A comparison of artificial neural networks and statistical analyses

    SciTech Connect

    Blough, D.K.; Anderson, K.K.

    1994-01-01

    Artificial neural networks have come to be used in a wide variety of data analytic applications, many of which were traditionally approached using statistical methods. It is the purpose of this paper to discuss the nature of the information obtained by each methodology, that of artificial neural networks and that of statistical analyses. Two aspects of the comparison will be considered: (1) what are the requirements needed for each approach in terms of model specification, data requirements, and computing power, and (2) what sort of information is contained in the results of each approach. Example analyses are presented characterizing the differences in the two approaches. A specific problem (hydrodynamic yield estimation) is presented with a corresponding data set. This data is then analyzed using statistical methods, and the results are compared with those obtained by using an artificial neural network. The requirements and results of the two approaches are then summarized as general guidelines an investigator can use in deciding which approach would be best for analyzing a given data set.

  20. The importance of artificial neural networks in biomedicine

    SciTech Connect

    Burke, H.B.

    1995-12-31

    The future explanatory power in biomedicine will be at the molecular-genetic level of analysis (rather than the epidemiologic-demographic or anatomic-cellular levels). This is the level of complex systems. Complex systems are characterized by nonlinearity and complex interactions. It is difficult for traditional statistical methods to capture complex systems because traditional methods attempt to find the model that best fits the statistician`s understanding of the phenomenon; complex systems are difficult to understand and therefore difficult to fit with a simple model. Artificial neural networks are nonparametric regression models. They can capture any phenomena, to any degree of accuracy (depending on the adequacy of the data and the power of the predictors), without prior knowledge of the phenomena. Further, artificial neural networks can be represented, not only as formulae, but also as graphical models. Graphical models can increase analytic power and flexibility. Artificial neural networks are a powerful method for capturing complex phenomena, but their use requires a paradigm shift, from exploratory analysis of the data to exploratory analysis of the model.

  1. Predicting developmental disorder in infants using an artificial neural network.

    PubMed

    Soleimani, Farin; Teymouri, Robab; Biglarian, Akbar

    2013-07-13

    Early recognition of developmental disorders is an important goal, and equally important is avoiding misdiagnosing a disorder in a healthy child without pathology. The aim of the present study was to develop an artificial neural network using perinatal information to predict developmental disorder at infancy. A total of 1,232 mother-child dyads were recruited from 6,150 in the original data of Karaj, Alborz Province, Iran. Thousands of variables are examined in this data including basic characteristics, medical history, and variables related to infants.  The validated Infant Neurological International Battery test was employed to assess the infant's development. The concordance indexes showed that true prediction of developmental disorder in the artificial neural network model, compared to the logistic regression model, was 83.1% vs. 79.5% and the area under ROC curves, calculated from testing data, were 0.79 and 0.68, respectively. In addition, specificity and sensitivity of the ANN model vs. LR model was calculated 93.2% vs. 92.7% and 39.1% vs. 21.7%. An artificial neural network performed significantly better than a logistic regression model.

  2. Visualizing the Hidden Activity of Artificial Neural Networks.

    PubMed

    Rauber, Paulo E; Fadel, Samuel G; Falcao, Alexandre X; Telea, Alexandru C

    2017-01-01

    In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.

  3. Background considerations in the analysis of PIXE spectra by Artificial Neural Systems.

    NASA Astrophysics Data System (ADS)

    Correa, R.; Morales, J. R.; Requena, I.; Miranda, J.; Barrera, V. A.

    2016-05-01

    In order to study the importance of background in PIXE spectra to determine elemental concentrations in atmospheric aerosols using artificial neural systems ANS, two independently trained ANS were constructed, one which considered as input the net number of counts in the peak, and another which included the background. In the training and validation phases thirty eight spectra of aerosols collected in Santiago, Chile, were used. In both cases the elemental concentration values were similar. This fact was due to the intrinsic characteristic of ANS operating with normalized values of the net and total number of counts under the peaks, something that was verified in the analysis of 172 spectra obtained from aerosols collected in Mexico city. Therefore, networks operating under the mode which include background can reduce time and cost when dealing with large number of samples.

  4. Development of a neural net paradigm that predicts simulator sickness

    SciTech Connect

    Allgood, G.O.

    1993-03-01

    A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

  5. Development of a neural net paradigm that predicts simulator sickness

    SciTech Connect

    Allgood, G.O.

    1993-03-01

    A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

  6. A TLD dose algorithm using artificial neural networks

    SciTech Connect

    Moscovitch, M.; Rotunda, J.E.; Tawil, R.A.; Rathbone, B.A.

    1995-12-31

    An artificial neural network was designed and used to develop a dose algorithm for a multi-element thermoluminescence dosimeter (TLD). The neural network architecture is based on the concept of functional links network (FLN). Neural network is an information processing method inspired by the biological nervous system. A dose algorithm based on neural networks is fundamentally different as compared to conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with given responses of a multi-element dosimeter (input) many times. The algorithm, being trained that way, eventually is capable to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personal dosimetry, the output consists of the desired dose components: deep dose, shallow dose and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. The neural network approach was applied to the Harshaw Type 8825 TLD, and was shown to significantly improve the performance of this dosimeter, well within the U.S. accreditation requirements for personnel dosimeters.

  7. Artificial neural network modeling of dissolved oxygen in reservoir.

    PubMed

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

  8. Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

    NASA Astrophysics Data System (ADS)

    Chattopadhyay, Surajit

    2007-09-01

    In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network model. In formulating the ANN — based predictive model, three-layer network has been constructed with sigmoid non-linearity. The monthly summer monsoon rainfall totals, tropical rainfall indices and sea surface temperature anomalies have been considered as predictors while generating the input matrix for the ANN. The data pertaining to the years 1950-1995 have been explored to develop the predictive model. Finally, the prediction performance of neural net has been compared with persistence forecast and Multiple Linear Regression forecast and the supremacy of the ANN has been established over the other processes.

  9. The Development of Animal Behavior: From Lorenz to Neural Nets

    NASA Astrophysics Data System (ADS)

    Bolhuis, Johan J.

    In the study of behavioral development both causal and functional approaches have been used, and they often overlap. The concept of ontogenetic adaptations suggests that each developmental phase involves unique adaptations to the environment of the developing animal. The functional concept of optimal outbreeding has led to further experimental evidence and theoretical models concerning the role of sexual imprinting in the evolutionary process of sexual selection. From a causal perspective it has been proposed that behavioral ontogeny involves the development of various kinds of perceptual, motor, and central mechanisms and the formation of connections among them. This framework has been tested for a number of complex behavior systems such as hunger and dustbathing. Imprinting is often seen as a model system for behavioral development in general. Recent advances in imprinting research have been the result of an interdisciplinary effort involving ethology, neuroscience, and experimental psychology, with a continual interplay between these approaches. The imprinting results are consistent with Lorenz' early intuitive suggestions and are also reflected in the architecture of recent neural net models.

  10. High solar activity predictions through an artificial neural network

    NASA Astrophysics Data System (ADS)

    Orozco-Del-Castillo, M. G.; Ortiz-Alemán, J. C.; Couder-Castañeda, C.; Hernández-Gómez, J. J.; Solís-Santomé, A.

    The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24-26, which we found to occur before 2040.

  11. An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

    DTIC Science & Technology

    1991-12-01

    Recurrent and feedforward artificial neural networks are developed as wavefront reconstructors. The recurrent neural network studied is the Hopfield...input features are just the wavefront sensor slope outputs. Both artificial neural networks use their inputs to calculate deformable mirror actuator commands. The effects of training are examined.

  12. Predicting stream water quality using artificial neural networks (ANN)

    SciTech Connect

    Bowers, J.A.

    2000-05-17

    Predicting point and nonpoint source runoff of dissolved and suspended materials into their receiving streams is important to protecting water quality and traditionally has been modeled using deterministic or statistical methods. The purpose of this study was to predict water quality in small streams using an Artificial Neural Network (ANN). The selected input variables were local precipitation, stream flow rates and turbidity for the initial prediction of suspended solids in the stream. A single hidden-layer feedforward neural network using backpropagation learning algorithms was developed with a detailed analysis of model design of those factors affecting successful implementation of the model. All features of a feedforward neural model were investigated including training set creation, number and layers of neurons, neural activation functions, and backpropagation algorithms. Least-squares regression was used to compare model predictions with test data sets. Most of the model configurations offered excellent predictive capabilities. Using either the logistic or the hyperbolic tangent neural activation function did not significantly affect predicted results. This was also true for the two learning algorithms tested, the Levenberg-Marquardt and Polak-Ribiere conjugate-gradient descent methods. The most important step during model development and training was the representative selection of data records for training of the model.

  13. Artificial neural networks applied to forecasting time series.

    PubMed

    Montaño Moreno, Juan J; Palmer Pol, Alfonso; Muñoz Gracia, Pilar

    2011-04-01

    This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fit regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research.

  14. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    SciTech Connect

    Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-03

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  15. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-01

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  16. Application of artificial neural network ensembles in probabilistic hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Araghinejad, Shahab; Azmi, Mohammad; Kholghi, Majid

    2011-09-01

    SummaryEnsemble techniques are used in regression/classification tasks with considerable success. Due to the flexible geometry of artificial neural networks (ANNs), they have been recognized as suitable models for ensemble techniques. The application of an ensemble technique is divided into two steps. The first step is to create individual ensemble members, and the second step is the appropriate combination of outputs of the ensemble members to produce the most appropriate output. This paper deals with the techniques of both generation and combination of ANN ensembles. A new performance function is proposed for generating neural network ensembles. Also a probabilistic method based on the K-nearest neighbor regression is proposed to combine individual networks and to improve the accuracy and precision of hydrological forecasts. The proposed method is applied on the peak discharge forecasting of the floods of Red River in Canada as well as the seasonal streamflow forecasting of Zayandeh-rud River in Iran. The study analyses the advantages of the proposed methods in comparison with the conventional empirical methods such as conventional artificial neural networks, and K-nearest neighbor regression. The utility of the proposed method for forecasting hydrological variables with a conditional probability distribution is demonstrated. The results of this study show that the application of the ensemble ANNs through the proposed method can improve the probabilistic forecast skill for hydrological events.

  17. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACHINE INDEPENDENT VERSION)

    NASA Technical Reports Server (NTRS)

    Baffes, P. T.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  18. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACINTOSH VERSION)

    NASA Technical Reports Server (NTRS)

    Phillips, T. A.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  19. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACHINE INDEPENDENT VERSION)

    NASA Technical Reports Server (NTRS)

    Baffes, P. T.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  20. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACINTOSH VERSION)

    NASA Technical Reports Server (NTRS)

    Phillips, T. A.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  1. Neural-Net Processed Characteristic Patterns for Measurement of Structural Integrity of Pressure Cycled Components

    NASA Technical Reports Server (NTRS)

    Decker, A. J.

    2001-01-01

    A neural-net inspection process has been combined with a bootstrap training procedure and electronic holography to detect changes or damage in a pressure-cycled International Space Station cold plate to be used for cooling instrumentation. The cold plate was excited to vibrate in a normal mode at low amplitude, and the neural net was trained by example to flag small changes in the mode shape. The NDE (nondestructive-evaluation) technique is straightforward but in its infancy; its applications are ad-hoc and uncalibrated. Nevertheless previous research has shown that the neural net can detect displacement changes to better than 1/100 the maximum displacement amplitude. Development efforts that support the NDE technique are mentioned briefly, followed by descriptions of electronic holography and neural-net processing. The bootstrap training procedure and its application to detection of damage in a pressure-cycled cold plate are discussed. Suggestions for calibrating and quantifying the NDE procedure are presented.

  2. Lexical Tone Recognition with an Artificial Neural Network

    PubMed Central

    Zhou, Ning; Zhang, Wenle; Lee, Chao-Yang; Xu, Li

    2008-01-01

    Objectives Tone production is particularly important for communicating in tone languages such as Mandarin Chinese. In the present study, an artificial neural network was used to recognize tones produced by adult native speakers. The purposes of the study were (1) to test the sensitivity of the neural network to speaker variation typically in adult speaker groups, (2) to evaluate two normalization procedures to overcome the effects of speaker variation, and (3) to compare tone recognition performance of the neural network with that of the human listeners. Design A feedforward multilayer neural network was used. Twenty-nine adult native Mandarin Chinese speakers were recruited to record tone samples. The F0 contours of the vowel part of the 1044 monosyllabic words recorded were extracted using an autocorrelation method. Samples from the F0 contours were used as inputs to the neural network. The efficacy of the neural network was first tested by varying the number of inputs and the number of neurons in the hidden layer from 1 to 16. The sensitivity of the neural network to speaker variation was tested by (1) using the raw F0 data from speech tokens of a number of randomly drawn speakers that varied from 1 to 29, (2) using the raw F0 data from speech tokens of either male-only or female-only speakers, and (3) using two sets of normalized F0 data (i.e., tone 1-based normalization and first-order derivative) from speech tokens from a number of randomly drawn speakers that varied from 1 to 29. The recognition performance of the neural network under several experimental conditions was compared with the corresponding recognition performance of 10 normal-hearing, native Mandarin Chinese speaking adult listeners. Results Three inputs and four hidden neurons were found to be sufficient for the neural network to perform at about 85% correct using speech samples without normalization. The performance of the neural network was affected by variation across speakers particularly

  3. Angle of Arrival Detection Through Artificial Neural Network Analysis of Optical Fiber Intensity Patterns

    DTIC Science & Technology

    1990-12-01

    ARTIFICIAL NEURAL NETWORK ANALYSIS OF OPTICAL FIBER INTENSITY PATTERNS THESIS Scott Thomas Captain, USAF AFIT/GE/ENG/90D-62 DTIC...ELECTE ao • JAN08 1991 Approved for public release; distribution unlimited. AFIT/GE/ENG/90D-62 ANGLE OF ARRIVAL DETECTION THROUGH ARTIFICIAL NEURAL NETWORK ANALYSIS... ARTIFICIAL NEURAL NETWORK ANALYSIS OF OPTICAL FIBER INTENSITY PATTERNS L Introduction The optical sensors of United States Air Force reconnaissance

  4. Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

    DTIC Science & Technology

    1998-05-01

    Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

  5. Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks

    DTIC Science & Technology

    2006-12-01

    Using Artificial Neural Networks Alexander Iversen, Nicholas K. Taylor and Keith E. Brown Intelligent Systems Laboratory Heriot-Watt University...Brown, K.E. (2006) Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks . In Military...Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM

  6. Science of the science, drug discovery and artificial neural networks.

    PubMed

    Patel, Jigneshkumar

    2013-03-01

    Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

  7. A brief overview and introduction to artificial neural networks.

    PubMed

    Buscema, Massimo

    2002-01-01

    This article is designed to acquaint professionals working in the field of substance use intervention with a range of artificial intelligence nonlinear, powerful tools, artificial neural networks, concepts, and paradigms. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data as well as our studying and understanding the many people, processes, and phenomena which comprise substance use and its intervention. The latter represent complex, dynamic, multidimensional phenomena which are unpredictable and uncontrollable in the traditional "cause and effect" sense. As such they are likely to be nonlinear in their very essence. Using linear-based paradigms for planned intervention with nonlinear phenomena brooks the all-too-common possibility of using inappropriate intervention paradigms and/or drawing misleading conclusions about what is and/or has happened.

  8. Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

    NASA Technical Reports Server (NTRS)

    Zee, Frank

    1995-01-01

    The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

  9. Prediction of Universal Time using the artificial neural network

    NASA Astrophysics Data System (ADS)

    Richard, J. Y.; Lopes, P.; Barache, C.; Bizouard, C.; Gambis, D.

    2014-12-01

    The monitoring of the Earth Orientation Parameters (EOP) variations is the main task of the Earth orientation Center of the IERS. In addition, for various applications linked in particular to navigation, precise orbit determination or leap seconds announcements, short and long term predictions are required. The method which is currently applied for predictions is based on deterministic processes, Least Square fitting, autoregressive filtering (Gambis and Luzum 2011). We present an alternative method, the Artificial Neural Networks (ANN) which has have already been successfully applied for pattern recognition. It has been tested as well by various authors for EOP predictions but with so far no real improvement compared to the current methods (Schuh et. al. 2002). New formalisms recently developed allow reconsidering the use of neural networks for EOP predictions. Series of simulations were performed for both short and long term predictions. Statistics and comparisons with the current methods are presented.

  10. Design of Jetty Piles Using Artificial Neural Networks

    PubMed Central

    2014-01-01

    To overcome the complication of jetty pile design process, artificial neural networks (ANN) are adopted. To generate the training samples for training ANN, finite element (FE) analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN) with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost. PMID:25177724

  11. Classification of cardiac patient states using artificial neural networks

    PubMed Central

    Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM

    2003-01-01

    Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222

  12. Artificial neural network for location estimation in wireless communication systems.

    PubMed

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  13. Experiments in dysarthric speech recognition using artificial neural networks.

    PubMed

    Jayaram, G; Abdelhamied, K

    1995-05-01

    In this study, we investigated the use of artificial neural networks (ANNs) to recognize dysarthric speech. Two multilayer neural networks were developed, trained, and tested using isolated words spoken by a dysarthric speaker. One network had the fast Fourier transform (FFT) coefficients as inputs, while the other network had the formant frequencies as inputs. The effect of additional features in the input vector on the recognition rate was also observed. The recognition rate was evaluated against the intelligibility rating obtained by five human listeners and also against the recognition rate of the Introvoice commercial speech-recognition system. Preliminary results demonstrated the ability of the developed networks to successfully recognize dysarthric speech despite its large variability. These networks clearly outperformed both the human listeners and the Introvoice commercial system.

  14. Hartmann wavefront sensing with an artificial neural network processor

    NASA Astrophysics Data System (ADS)

    Atad-Ettedgui, Eli I.; Catalan, G.; Harris, John W.; Humphries, Colin M.; Smillie, A. M.; Armitage, Alistair E.; Hanspal, G. S.; McKeating, R. J.

    1994-05-01

    An artificial neural network has been developed for wavefront reconstruction by processing the centroid displacements of the subaperture images of a 4 X 4 Hartmann sensor. Training was accomplished by computing the first differentials of random but known mixes of aberration polynomials, presenting these at the network input and adjusting the interconnection weights to minimize the Zernike coefficient output errors. After training, the residual rms wavefront errors for noise-free systems were typically within 2%. Similar results were obtained with a 5 X 5 system. As simulated noise was added, the errors increased slowly but were similar in magnitude to those obtained analytically provided that the network had sufficient complexity. At very low SNR values the neural network outperformed the analytic method provided that it had been trained for the same noise level.

  15. Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

    NASA Technical Reports Server (NTRS)

    Zee, Frank

    1995-01-01

    The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

  16. Using Artificial Neural Networks to Assess Microbial Communities

    SciTech Connect

    Almeida, J.S.; Brand, C.C.; Palumbo, A.V.; Pfiffner, S.M.; Schryver, J.C.

    1998-09-08

    We are evaluating artificial neural networks (ANNs) as tools for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker data collected from two soil microcosm experiments using an autoassociative ANN. In one experiment, the microcosms were exposed to O, 100, or 250 ppm of metals, and in the other experiment the microcosms were exposed to O or 500 ppm of metals. The ANNs were able to distinguish between microcosms exposed and not exposed to metals in both experiments.

  17. Communication: Separable potential energy surfaces from multiplicative artificial neural networks

    SciTech Connect

    Koch, Werner Zhang, Dong H.

    2014-07-14

    We present a potential energy surface fitting scheme based on multiplicative artificial neural networks. It has the sum of products form required for efficient computation of the dynamics of multidimensional quantum systems with the multi configuration time dependent Hartree method. Moreover, it results in analytic potential energy matrix elements when combined with quantum dynamics methods using Gaussian basis functions, eliminating the need for a local harmonic approximation. Scaling behavior with respect to the complexity of the potential as well as the requested accuracy is discussed.

  18. Weld modeling and control using artificial neural networks

    SciTech Connect

    Cook, G.E.; Barnett, R.J.; Andersen, K.; Strauss, A.M.

    1995-11-01

    The arc welding processes play an important role in modern manufacturing. Despite the widespread use of arc welding for joining metals, controlling most welding processes still requires considerable skills and experience on behalf of the human welder. Total automation of welding has not yet been achieved, largely because the physics which determine the success of any welding task, are not yet fully understood and quantified. Artificial neural networks were evaluated for monitoring and control of the variable polarity plasma arc welding process. Three areas of welding application were investigated: weld process modeling, weld process control, and weld bead profile analysis for quality control.

  19. Simulation of nonlinear strutures with artificial neural networks

    SciTech Connect

    Paez, T.L.

    1996-03-01

    Structural system simulation is important in analysis, design, testing, control, and other areas, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks offer a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and simulation a much simpler problem. A numerical example is also presented.

  20. Video data compression using artificial neural network differential vector quantization

    NASA Technical Reports Server (NTRS)

    Krishnamurthy, Ashok K.; Bibyk, Steven B.; Ahalt, Stanley C.

    1991-01-01

    An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

  1. Artificial neural networks in Space Station optimal attitude control

    NASA Astrophysics Data System (ADS)

    Kumar, Renjith R.; Seywald, Hans; Deshpande, Samir M.; Rahman, Zia

    1992-08-01

    Innovative techniques of using 'Artificial Neural Networks' (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using Control Moment Gyros (CMGs) are investigated. The first technique uses a feedforward ANN with multilayer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second techique uses a single layer feedforward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.

  2. Adaptive conventional power system stabilizer based on artificial neural network

    SciTech Connect

    Kothari, M.L.; Segal, R.; Ghodki, B.K.

    1995-12-31

    This paper deals with an artificial neural network (ANN) based adaptive conventional power system stabilizer (PSS). The ANN comprises an input layer, a hidden layer and an output layer. The input vector to the ANN comprises real power (P) and reactive power (Q), while the output vector comprises optimum PSS parameters. A systematic approach for generating training set covering wide range of operating conditions, is presented. The ANN has been trained using back-propagation training algorithm. Investigations reveal that the dynamic performance of ANN based adaptive conventional PSS is quite insensitive to wide variations in loading conditions.

  3. Hybrid multiobjective evolutionary design for artificial neural networks.

    PubMed

    Goh, Chi-Keong; Teoh, Eu-Jin; Tan, Kay Chen

    2008-09-01

    Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide superior performance over conventional training approaches, the simultaneous optimization of network performance and architecture will almost always result in a slow training process due to the added algorithmic complexity. In this paper, we present a geometrical measure based on the singular value decomposition (SVD) to estimate the necessary number of neurons to be used in training a single-hidden-layer feedforward neural network (SLFN). In addition, we develop a new hybrid multiobjective evolutionary approach that includes the features of a variable length representation that allow for easy adaptation of neural networks structures, an architectural recombination procedure based on the geometrical measure that adapts the number of necessary hidden neurons and facilitates the exchange of neuronal information between candidate designs, and a microhybrid genetic algorithm ( microHGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, the performances of well-known algorithms as well as the effectiveness and contributions of the proposed approach are analyzed and validated through a variety of data set types.

  4. Autonomous Defensive Space Control via On-Board Artificial Neural Networks

    DTIC Science & Technology

    2007-04-01

    by investing the necessary resources for the development of space-based neural networks . An artificial neural network (ANN) or commonly just neural...processing capability could potentially enable the placement of neural networks , requiring significant processing power and storage capacity, on-board

  5. DeepNet: An Ultrafast Neural Learning Code for Seismic Imaging

    SciTech Connect

    Barhen, J.; Protopopescu, V.; Reister, D.

    1999-07-10

    A feed-forward multilayer neural net is trained to learn the correspondence between seismic data and well logs. The introduction of a virtual input layer, connected to the nominal input layer through a special nonlinear transfer function, enables ultrafast (single iteration), near-optimal training of the net using numerical algebraic techniques. A unique computer code, named DeepNet, has been developed, that has achieved, in actual field demonstrations, results unattainable to date with industry standard tools.

  6. Identification chemical agent simulants by remote infrared spectra with improved artificial neural network

    NASA Astrophysics Data System (ADS)

    Dong, Xiao-qiang; Guo, Teng-xiao; Yan, Yu-Ting; Wang, Ji; Zhang, Xu; Li, Jun-ming

    2016-03-01

    Remote infrared sensing is a good approach to detect Chemical agents which can prevent operator being poisoned. The pattern recognition algorithms such as artificial neural network are the core of the chemical agent spectra identification subsystem. This paper presents a modified artificial neural network that can effectively train and identify chemical agent remote sensing spectra. The C++ language was used to program the identification software. Then many remote sensing spectra DMMP as chemical agent simulants were used to train the artificial neural network. The results show that the adaptive momentum and adaptive learning rate accelerate the artificial neural network convergence, cross-examination avoids neural network over-fitting, and the modified artificial neural network can be used to identify chemical agents remote sensing spectra perfectly.

  7. Landslide susceptibility analysis using an artificial neural network model

    NASA Astrophysics Data System (ADS)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  8. Network traffic anomaly prediction using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Ciptaningtyas, Hening Titi; Fatichah, Chastine; Sabila, Altea

    2017-03-01

    As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.

  9. Risk prediction model: Statistical and artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  10. Investigation of rat exploratory behavior via evolving artificial neural networks.

    PubMed

    Costa, Ariadne de Andrade; Tinós, Renato

    2016-09-01

    Neuroevolution comprises the use of evolutionary computation to define the architecture and/or to train artificial neural networks (ANNs). This strategy has been employed to investigate the behavior of rats in the elevated plus-maze, which is a widely used tool for studying anxiety in mice and rats. Here we propose a neuroevolutionary model, in which both the weights and the architecture of artificial neural networks (our virtual rats) are evolved by a genetic algorithm. This model is an improvement of a previous model that involves the evolution of just the weights of the ANN by the genetic algorithm. In order to compare both models, we analyzed traditional measures of anxiety behavior, like the time spent and the number of entries in both open and closed arms of the maze. When compared to real rat data, our findings suggest that the results from the model introduced here are statistically better than those from other models in the literature. In this way, the neuroevolution of architecture is clearly important for the development of the virtual rats. Moreover, this technique allowed the comprehension of the importance of different sensory units and different number of hidden neurons (performing as memory) in the ANNs (virtual rats). Copyright © 2016 Elsevier B.V. All rights reserved.

  11. EEG dipole source localization using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Van Hoey, Gert; DeClercq, Jeremy; Vanrumste, Bart; Van de Walle, Rik; Lemahieu, Ignace; D'Havé, Michel; Boon, Paul

    2000-04-01

    Localization of focal electrical activity in the brain using dipole source analysis of the electroencephalogram (EEG), is usually performed by iteratively determining the location and orientation of the dipole source, until optimal correspondence is reached between the dipole source and the measured potential distribution on the head. In this paper, we investigate the use of feed-forward layered artificial neural networks (ANNs) to replace the iterative localization procedure, in order to decrease the calculation time. The localization accuracy of the ANN approach is studied within spherical and realistic head models. Additionally, we investigate the robustness of both the iterative and the ANN approach by observing the influence on the localization error of both noise in the scalp potentials and scalp electrode mislocalizations. Finally, after choosing the ANN structure and size that provides a good trade-off between low localization errors and short computation times, we compare the calculation times involved with both the iterative and ANN methods. An average localization error of about 3.5 mm is obtained for both spherical and realistic head models. Moreover, the ANN localization approach appears to be robust to noise and electrode mislocations. In comparison with the iterative localization, the ANN provides a major speed-up of dipole source localization. We conclude that an artificial neural network is a very suitable alternative for iterative dipole source localization in applications where large numbers of dipole localizations have to be performed, provided that an increase of the localization errors by a few millimetres is acceptable.

  12. Applications of artificial neural networks in medical science.

    PubMed

    Patel, Jigneshkumar L; Goyal, Ramesh K

    2007-09-01

    Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

  13. Artificial neural network based particle size prediction of polymeric nanoparticles.

    PubMed

    Youshia, John; Ali, Mohamed Ehab; Lamprecht, Alf

    2017-10-01

    Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Sh; Kalhor, E. G.; Nabavi, S. R.; Alamiparvin, L.; Pogaku, R.

    2016-06-01

    In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.

  15. Neuron-Glia Interactions in Neural Plasticity: Contributions of Neural Extracellular Matrix and Perineuronal Nets

    PubMed Central

    Dzyubenko, Egor; Gottschling, Christine

    2016-01-01

    Synapses are specialized structures that mediate rapid and efficient signal transmission between neurons and are surrounded by glial cells. Astrocytes develop an intimate association with synapses in the central nervous system (CNS) and contribute to the regulation of ion and neurotransmitter concentrations. Together with neurons, they shape intercellular space to provide a stable milieu for neuronal activity. Extracellular matrix (ECM) components are synthesized by both neurons and astrocytes and play an important role in the formation, maintenance, and function of synapses in the CNS. The components of the ECM have been detected near glial processes, which abut onto the CNS synaptic unit, where they are part of the specialized macromolecular assemblies, termed perineuronal nets (PNNs). PNNs have originally been discovered by Golgi and represent a molecular scaffold deposited in the interface between the astrocyte and subsets of neurons in the vicinity of the synapse. Recent reports strongly suggest that PNNs are tightly involved in the regulation of synaptic plasticity. Moreover, several studies have implicated PNNs and the neural ECM in neuropsychiatric diseases. Here, we highlight current concepts relating to neural ECM and PNNs and describe an in vitro approach that allows for the investigation of ECM functions for synaptogenesis. PMID:26881114

  16. Simulation of an array-based neural net model

    NASA Technical Reports Server (NTRS)

    Barnden, John A.

    1987-01-01

    Research in cognitive science suggests that much of cognition involves the rapid manipulation of complex data structures. However, it is very unclear how this could be realized in neural networks or connectionist systems. A core question is: how could the interconnectivity of items in an abstract-level data structure be neurally encoded? The answer appeals mainly to positional relationships between activity patterns within neural arrays, rather than directly to neural connections in the traditional way. The new method was initially devised to account for abstract symbolic data structures, but it also supports cognitively useful spatial analogue, image-like representations. As the neural model is based on massive, uniform, parallel computations over 2D arrays, the massively parallel processor is a convenient tool for simulation work, although there are complications in using the machine to the fullest advantage. An MPP Pascal simulation program for a small pilot version of the model is running.

  17. Limited-angle tomography using artificial neural network

    NASA Astrophysics Data System (ADS)

    Yau, Sze-Fong; Wong, Shun-Him

    1996-03-01

    This paper considers the problem of limited angle tomography in which a complete sinogram is not available. This situation arises in many practical applications where tomographic projection over 180 degrees is either physically unrealizable or infeasible. When a complete sinogram is not available, it is well known that the reconstructed images using common reconstruction algorithms, such as convolution back projection (CBP), will have severe streak artifacts. In this paper, we present a linear artificial neural network to extrapolate the missing part of the sinogram. Once the complete sinogram is obtained via extrapolation, standard reconstruction techniques such as CBP can be used to generate artifact free reconstructions. The parameters of the neural network are designed using the sampling theory of signals with non-compact spectral support, the knowledge that complete sinograms have bowtie-shaped spectral support, and regularization. It is found that once designed, these parameters are data independent, especially for images of similar nature. For sinogram with 2N angular views, each having M raysum per view, if 2L views are available, the computational requirement of the neural network is 4MNL only. Hence, it is much more efficient than other iterative algorithms such as the method of projection onto convex sets, the Papoulis-Gerchberg's algorithm and the Clark-Palmer-Lawrence interpolation method.

  18. Bootstrapped neural nets versus regression kriging in the digital mapping of pedological attributes: the automatic and time-consuming perspectives

    NASA Astrophysics Data System (ADS)

    Langella, Giuliano; Basile, Angelo; Bonfante, Antonello; Manna, Piero; Terribile, Fabio

    2013-04-01

    Digital soil mapping procedures are widespread used to build two-dimensional continuous maps about several pedological attributes. Our work addressed a regression kriging (RK) technique and a bootstrapped artificial neural network approach in order to evaluate and compare (i) the accuracy of prediction, (ii) the susceptibility of being included in automatic engines (e.g. to constitute web processing services), and (iii) the time cost needed for calibrating models and for making predictions. Regression kriging is maybe the most widely used geostatistical technique in the digital soil mapping literature. Here we tried to apply the EBLUP regression kriging as it is deemed to be the most statistically sound RK flavor by pedometricians. An unusual multi-parametric and nonlinear machine learning approach was accomplished, called BAGAP (Bootstrap aggregating Artificial neural networks with Genetic Algorithms and Principal component regression). BAGAP combines a selected set of weighted neural nets having specified characteristics to yield an ensemble response. The purpose of applying these two particular models is to ascertain whether and how much a more cumbersome machine learning method could be much promising in making more accurate/precise predictions. Being aware of the difficulty to handle objects based on EBLUP-RK as well as BAGAP when they are embedded in environmental applications, we explore the susceptibility of them in being wrapped within Web Processing Services. Two further kinds of aspects are faced for an exhaustive evaluation and comparison: automaticity and time of calculation with/without high performance computing leverage.

  19. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

    PubMed

    Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

    2017-03-01

    Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks.

  20. A new method for COD analysis with full-spectrum based on Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Feng, Wei-wei; Li, Dan; Cai, Zong-qi; Hao, Fu-guo

    2016-09-01

    A new on-line monitoring system was developed for the determination of chemical oxygen demand (COD) in water based on full-spectrum analysis. In this system, Artificial Neural Net (ANN) work was used to obtain the transmission equation between absorbance and COD value by measuring absorption spectra of water with known COD value, and then the established equation could inverse the COD values of the unknown water samples. For the COD determination of simulated complicated water samples, the instrumental reliability was well validated by a comparison made between the ANN method and the PLS method. The monitoring system of the ANN method provided advantages of simplicity, rapidity, high precision, no consumption of reagent. And it was demonstrated an ideal alternative to real-time and on-line monitoring of COD in water.

  1. The utilization of neural nets in populating an object-oriented database

    NASA Technical Reports Server (NTRS)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms (i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  2. Extraction of principal components from biosignals by neural net.

    PubMed

    Krajca, V; Principe, J C; Petránek, S

    1999-01-01

    This contribution gives the information on a useful application of principal component analysis (PCA) in the field of electroencephalogram (EEG) and laser-Doppler signal processing. The principal components are estimated by a neural network (NN) approach.

  3. Larger bases and mixed analog/digital neural nets

    SciTech Connect

    Beiu, V.

    1998-12-31

    The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.

  4. Identifing Atmospheric Pollutant Sources Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Paes, F. F.; Campos, H. F.; Luz, E. P.; Carvalho, A. R.

    2008-05-01

    The estimation of the area source pollutant strength is a relevant issue for atmospheric environment. This characterizes an inverse problem in the atmospheric pollution dispersion. In the inverse analysis, an area source domain is considered, where the strength of such area source term is assumed unknown. The inverse problem is solved by using a supervised artificial neural network: multi-layer perceptron. The conection weights of the neural network are computed from delta rule - learning process. The neural network inversion is compared with results from standard inverse analysis (regularized inverse solution). In the regularization method, the inverse problem is formulated as a non-linear optimization approach, whose the objective function is given by the square difference between the measured pollutant concentration and the mathematical models, associated with a regularization operator. In our numerical experiments, the forward problem is addressed by a source-receptor scheme, where a regressive Lagrangian model is applied to compute the transition matrix. The second order maximum entropy regularization is used, and the regularization parameter is calculated by the L-curve technique. The objective function is minimized employing a deterministic scheme (a quasi-Newton algorithm) [1] and a stochastic technique (PSO: particle swarm optimization) [2]. The inverse problem methodology is tested with synthetic observational data, from six measurement points in the physical domain. The best inverse solutions were obtained with neural networks. References: [1] D. R. Roberti, D. Anfossi, H. F. Campos Velho, G. A. Degrazia (2005): Estimating Emission Rate and Pollutant Source Location, Ciencia e Natura, p. 131-134. [2] E.F.P. da Luz, H.F. de Campos Velho, J.C. Becceneri, D.R. Roberti (2007): Estimating Atmospheric Area Source Strength Through Particle Swarm Optimization. Inverse Problems, Desing and Optimization Symposium IPDO-2007, April 16-18, Miami (FL), USA, vol 1, p

  5. [The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

    PubMed

    Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

    2016-03-01

    The Fourier Transform Infrared Spectroscopy (FTIR) is established to find the geographic origins of Chinese wolfberry quickly. In the paper, the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR. The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform. Compared with common windows shifting smoothing preprocessing, standard normal variation correction and multiplicative scatter correction, wavelet transform is an effective spectrum data preprocessing method. Before establishing model through the artificial neural networks, the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks, and at the same time the related parameters of the artificial neural networks model are also discussed in detail. The survey shows even if the infrared spectroscopy data is compressed to 1/8 of its original data, the spectral information and analytical accuracy are not deteriorated. The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network (BP-ANN) model and the geographic origins of Chinese wolfberry are used for parameters of export. Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network. The number of hidden layer neurons is 5, and the number of output layer neuron is 1. The transfer function of hidden layer is tansig, while the transfer function of output layer is purelin. Network training function is trainl and the learning function of weights and thresholds is learngdm. net. trainParam. epochs=1 000, while net. trainParam. goal = 0.001. The recognition rate of 100% is to be achieved. It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry. The infrared spectral analysis technology

  6. Practical application of artificial neural networks in the neurosciences

    NASA Astrophysics Data System (ADS)

    Pinti, Antonio

    1995-04-01

    This article presents a practical application of artificial multi-layer perceptron (MLP) neural networks in neurosciences. The data that are processed are labeled data from the visual analysis of electrical signals of human sleep. The objective of this work is to automatically classify into sleep stages the electrophysiological signals recorded from electrodes placed on a sleeping patient. Two large data bases were designed by experts in order to realize this study. One data base was used to train the network and the other to test its generalization capacity. The classification results obtained with the MLP network were compared to a type K nearest neighbor Knn non-parametric classification method. The MLP network gave a better result in terms of classification than the Knn method. Both classification techniques were implemented on a transputer system. With both networks in their final configuration, the MLP network was 160 times faster than the Knn model in classifying a sleep period.

  7. Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Chauhan, Shivani; Sharma, Mukta; Arora, M. K.; Gupta, N. K.

    2010-10-01

    In the present study, Artificial Neural Network (ANN) has been implemented to derive ratings of categories of causative factors, which are then integrated to produce a landslide susceptibility zonation map in an objective manner. The results have been evaluated with an ANN based black box approach for Landslide Susceptibility Zonation (LSZ) proposed earlier by the authors. Seven causative factors, namely, slope, slope aspect, relative relief, lithology, structural features (e.g., thrusts and faults), landuse landcover, and drainage density, were placed in 42 categories for which ratings were determined. The results indicate that LSZ map based on ratings derived from ANN performs exceedingly better than that produced from the earlier ANN based approach. The landslide density analysis clearly showed that susceptibility zones were in close agreement with actual landslide areas in the field.

  8. Application of artificial neural networks in computer-aided diagnosis.

    PubMed

    Liu, Bei

    2015-01-01

    Computer-aided diagnosis is a diagnostic procedure in which a radiologist uses the outputs of computer analysis of medical images as a second opinion in the interpretation of medical images, either to help with lesion detection or to help determine if the lesion is benign or malignant. Artificial neural networks (ANNs) are usually employed to formulate the statistical models for computer analysis. Receiver operating characteristic curves are used to evaluate the performance of the ANN alone, as well as the diagnostic performance of radiologists who take into account the ANN output as a second opinion. In this chapter, we use mammograms to illustrate how an ANN model is trained, tested, and evaluated, and how a radiologist should use the ANN output as a second opinion in CAD.

  9. Applications of artificial neural networks (ANNs) in food science.

    PubMed

    Huang, Yiqun; Kangas, Lars J; Rasco, Barbara A

    2007-01-01

    Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.

  10. Incomplete fuzzy data processing systems using artificial neural network

    NASA Technical Reports Server (NTRS)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  11. Inflow forecasting using Artificial Neural Networks for reservoir operation

    NASA Astrophysics Data System (ADS)

    Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Bankaru-Swamy, Soundharajan

    2016-05-01

    In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.

  12. Application of artificial neural networks for prediction of photocatalytic reactor.

    PubMed

    Delnavaz, Mohammad

    2015-02-01

    In this paper, forecasting of kinetic constant and efficiency of photocatalytic process of TiO2 nano powder immobilized on light expanded clay aggregates (LECA) was investigated. Synthetic phenolic wastewater, which is toxic and not easily biodegradable, was selected as the pollutant. The efficiency of the process in various operation conditions, including initial phenol concentration, pH, TiO2 concentration, retention time, and UV lamp intensity, was then measured. The TiO2 nano powder was immobilized on LECA using slurry and sol-gel methods. Kinetics of photocatalytic reactions has been proposed to follow the Langmuir-Hinshelwood model in different initial phenol concentration and pH. Several steps of training and testing of the models were used to determine the appropriate architecture of the artificial neural network models (ANNs). The ANN-based models were found to provide an efficient and robust tool in predicting photocatalytic reactor efficiency and kinetic constant for treating phenolic compounds.

  13. Nuclear power plant status diagnostics using artificial neural networks

    SciTech Connect

    Bartlett, E.B. . Dept. of Mechanical Engineering); Uhrig, R.E. . Dept. of Nuclear Engineering)

    1991-01-01

    In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.

  14. Nuclear power plant status diagnostics using artificial neural networks

    SciTech Connect

    Bartlett, E.B.; Uhrig, R.E.

    1991-12-31

    In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.

  15. Magnesium degradation as determined by artificial neural networks.

    PubMed

    Willumeit, Regine; Feyerabend, Frank; Huber, Norbert

    2013-11-01

    Magnesium degradation under physiological conditions is a highly complex process in which temperature, the use of cell culture growth medium and the presence of CO2, O2 and proteins can influence the corrosion rate and the composition of the resulting corrosion layer. Due to the complexity of this process it is almost impossible to predict the parameters that are most important and whether some parameters have a synergistic effect on the corrosion rate. Artificial neural networks are a mathematical tool that can be used to approximate and analyse non-linear problems with multiple inputs. In this work we present the first analysis of corrosion data obtained using this method, which reveals that CO2 and the composition of the buffer system play a crucial role in the corrosion of magnesium, whereas O2, proteins and temperature play a less prominent role.

  16. Magnetotelluric inversion for azimuthally anisotropic resistivities employing artificial neural networks

    NASA Astrophysics Data System (ADS)

    Montahaei, Mansoure; Oskooi, Behrooz

    2014-02-01

    An extension of an artificial neural network (ANN) approach to solve the magnetotelluric (MT) inverse problem for azimuthally anisotropic resistivities is presented and applied for a real dataset. Three different model classes, containing general 1-D and 2-D azimuthally anisotropic features, have been considered. For each model class, characteristics of three-layer feed forward ANNs trained through an error back propagation algorithm have been adjusted to approximate the inverse modeling function. It appears that, at least for synthetic models, reasonable results would be obtained by applying the amplitudes of the complex impedance tensor elements as inputs. Furthermore, the Levenberg-Marquart algorithm possesses optimal performance as a learning paradigm for this problem. The evaluation of applicability of the trained ANNs for unknown data sets excluded from the learning procedure reveals that the trained ANNs possess acceptable interpolation and extrapolation abilities to estimate model parameters accurately. This method was also successfully used for a field dataset wherein anisotropy had been previously recognized.

  17. Artificial neural network for the configuration problem in solids

    NASA Astrophysics Data System (ADS)

    Ji, Hyunjun; Jung, Yousung

    2017-02-01

    A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R2 = 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R2 = 0.95 and MAD = 0.13 eV.

  18. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    PubMed

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2016-09-30

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  19. Artificial neural networks in Space Station optimal attitude control

    NASA Astrophysics Data System (ADS)

    Kumar, Renjith R.; Seywald, Hans; Deshpande, Samir M.; Rahman, Zia

    1995-01-01

    Innovative techniques of using "artificial neural networks" (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using control moment gyros (CMGs) are investigated. The first technique uses a feed-forward ANN with multi-layer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second technique uses a single layer feed-forward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.

  20. Semantic characteristics prediction of pulmonary nodule using Artificial Neural Networks.

    PubMed

    Li, Guangxu; Kim, Hyoungseop; Tan, Joo Kooi; Ishikawa, Seiji; Hirano, Yasushi; Kido, Shoji; Tachibana, Rie

    2013-01-01

    Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately.

  1. Fast tomographic reconstruction from limited data using artificial neural networks.

    PubMed

    Pelt, Daniël Maria; Batenburg, Kees Joost

    2013-12-01

    Image reconstruction from a small number of projections is a challenging problem in tomography. Advanced algorithms that incorporate prior knowledge can sometimes produce accurate reconstructions, but they typically require long computation times. Furthermore, the required prior knowledge can be very specific, limiting the type of images that can be reconstructed. Here, we present a reconstruction method that automatically learns prior knowledge using an artificial neural network. We show that this method can be viewed as a combination of filtered backprojection steps, and, therefore, has a relatively low computational cost. Results for two different cases show that the new method is able to use the learned information to produce high quality reconstructions in a short time, even when presented with a small number of projections.

  2. Artificial metaplasticity neural network applied to credit scoring.

    PubMed

    Marcano-Cedeño, Alexis; Marin-de-la-Barcena, A; Jimenez-Trillo, J; Piñuela, J A; Andina, D

    2011-08-01

    The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.

  3. Applications of Artificial Neural Networks (ANNs) in Food Science

    SciTech Connect

    HUang, Yiqun; Kangas, Lars J.; Rasco, Barbara A.

    2007-02-01

    Abstract Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decade, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs have a great deal of promise for modeling complex tasks in process control and simulation, and in applications of machine perception including machine vision and the electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in this field.

  4. ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Collister, Adrian A.; Lahav, Ofer

    2004-04-01

    We introduce ANNz, a freely available software package for photometric redshift estimation using artificial neural networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available, ANNz is a highly competitive tool when compared with traditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the rms redshift error in the range 0<~z<~0.7 is σrms=0.023. Nonideal conditions (spectroscopic sets that are small or brighter than the photometric set for which redshifts are required) are simulated, and the impact on the photometric redshift accuracy is assessed.2

  5. Incomplete fuzzy data processing systems using artificial neural network

    NASA Technical Reports Server (NTRS)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  6. Automatic segmentation of cerebral MR images using artificial neural networks

    SciTech Connect

    Alirezaie, J.; Jernigan, M.E.; Nahmias, C.

    1996-12-31

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem.

  7. Prediction of Dried Durian Moisture Content Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Husna, Marati; Purqon, Acep

    2016-08-01

    Moisture content has a crucial issue in post-harvest processing since it plays main role to estimate a quality of dried product. However, estimating the moisture content is difficult since it shows mathematically nonlinear systems and complex physical processes. We investigate the prediction of moisture content of dried product by using Artificial Neural Networks (ANN). Our sample is a Bengkulu's local durian that is dried using a microwave oven. Our results show that ANN can predict the moisture content by performing with R2 value is 98.47%. Moreover, the RMSE values is 3.97% and MSE values is 0.16%. Our results indicate that ANN model have high capability for predicting moisture content and it is potentially applied in post-harvest product, especially in drying product quality control.

  8. Interpretation of artificial neural networks by means of fuzzy rules.

    PubMed

    Castro, J L; Mantas, C J; Benitez, J M

    2002-01-01

    This paper presents an extension of the method presented by Benitez et al (1997) for extracting fuzzy rules from an artificial neural network (ANN) that express exactly its behavior. The extraction process provides an interpretation of the ANN in terms of fuzzy rules. The fuzzy rules presented are in accordance with the domain of the input variables. These rules use a new operator in the antecedent. The properties and intuitive meaning of this operator are studied. Next, the role of the biases in the fuzzy rule-based systems is analyzed. Several examples are presented to comment on the obtained fuzzy rule-based systems. Finally, the interpretation of ANNs with two or more hidden layers is also studied.

  9. Direct Inverse Control using an Artificial Neural Network for the Autonomous Hover of a Helicopter

    DTIC Science & Technology

    2014-10-05

    Inverse Control technique using an Artificial Neural Network to learn and then cancel out the Hover dynamics of the quadrotor UAV... Inverse Control , Neural Network , Flight Control , and UAV helicopter REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR...Helicopter. The goal of the project is to investigate the effectiveness of the Direct Inverse Control technique using an Artificial Neural Network to

  10. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    SciTech Connect

    Saini, K. K.; Saini, Sanju

    2008-10-07

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  11. The Application of Neural Networks with Artificial Intelligence Technique in the Modeling of Industrial Processes

    NASA Astrophysics Data System (ADS)

    Saini, K. K.; Saini, Sanju

    2008-10-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can "learn," automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  12. Use of artificial neural networks in the identification and classification of tomatoes

    NASA Astrophysics Data System (ADS)

    Zaborowicz, M.; Boniecki, P.; Koszela, K.; Przybył, J.; Mazur, Robert; Kujawa, S.; Pilarski, K.

    2013-07-01

    The project aimed to produce a classification model of neural network that would allow automatic evaluate quality of greenhouse tomatoes. The project used computer image analysis and artificial neural networks. Authors based on the analysis of biological material selected set of features that are describing the physical parameters allowing the quality class identification. Image analysis of tomatoes digital photographs samples allowed to choose characteristics features. Obtained characteristics from the images were used as learning data for artificial neural network.

  13. Review of Artificial Neural Networks (ANN) applied to corrosion monitoring

    NASA Astrophysics Data System (ADS)

    Mabbutt, S.; Picton, P.; Shaw, P.; Black, S.

    2012-05-01

    The assessment of corrosion within an engineering system often forms an important aspect of condition monitoring but it is a parameter that is inherently difficult to measure and predict. The electrochemical nature of the corrosion process allows precise measurements to be made. Advances in instruments, techniques and software have resulted in devices that can gather data and perform various analysis routines that provide parameters to identify corrosion type and corrosion rate. Although corrosion rates are important they are only useful where general or uniform corrosion dominates. However, pitting, inter-granular corrosion and environmentally assisted cracking (stress corrosion) are examples of corrosion mechanisms that can be dangerous and virtually invisible to the naked eye. Electrochemical noise (EN) monitoring is a very useful technique for detecting these types of corrosion and it is the only non-invasive electrochemical corrosion monitoring technique commonly available. Modern instrumentation is extremely sensitive to changes in the system and new experimental configurations for gathering EN data have been proven. In this paper the identification of localised corrosion by different data analysis routines has been reviewed. In particular the application of Artificial Neural Network (ANN) analysis to corrosion data is of key interest. In most instances data needs to be used with conventional theory to obtain meaningful information and relies on expert interpretation. Recently work has been carried out using artificial neural networks to investigate various types of corrosion data in attempts to predict corrosion behaviour with some success. This work aims to extend this earlier work to identify reliable electrochemical indicators of localised corrosion onset and propagation stages.

  14. Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

    2016-03-01

    For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

  15. Gust prediction via artificial hair sensor array and neural network

    NASA Astrophysics Data System (ADS)

    Pankonien, Alexander M.; Thapa Magar, Kaman S.; Beblo, Richard V.; Reich, Gregory W.

    2017-04-01

    Gust Load Alleviation (GLA) is an important aspect of flight dynamics and control that reduces structural loadings and enhances ride quality. In conventional GLA systems, the structural response to aerodynamic excitation informs the control scheme. A phase lag, imposed by inertia, between the excitation and the measurement inherently limits the effectiveness of these systems. Hence, direct measurement of the aerodynamic loading can eliminate this lag, providing valuable information for effective GLA system design. Distributed arrays of Artificial Hair Sensors (AHS) are ideal for surface flow measurements that can be used to predict other necessary parameters such as aerodynamic forces, moments, and turbulence. In previous work, the spatially distributed surface flow velocities obtained from an array of artificial hair sensors using a Single-State (or feedforward) Neural Network were found to be effective in estimating the steady aerodynamic parameters such as air speed, angle of attack, lift and moment coefficient. This paper extends the investigation of the same configuration to unsteady force and moment estimation, which is important for active GLA control design. Implementing a Recurrent Neural Network that includes previous-timestep sensor information, the hair sensor array is shown to be capable of capturing gust disturbances with a wide range of periods, reducing predictive error in lift and moment by 68% and 52% respectively. The L2 norms of the first layer of the weight matrices were compared showing a 23% emphasis on prior versus current information. The Recurrent architecture also improves robustness, exhibiting only a 30% increase in predictive error when undertrained as compared to a 170% increase by the Single-State NN. This diverse, localized information can thus be directly implemented into a control scheme that alleviates the gusts without waiting for a structural response or requiring user-intensive sensor calibration.

  16. Minimax design of neural net controllers for highly uncertain plants.

    PubMed

    Sebald, A V; Schlenzig, J

    1994-01-01

    This paper discusses the use of evolutionary programming (EP) for computer-aided design and testing of neural controllers applied to problems in which the system to be controlled is highly uncertain. Examples include closed-loop control of drug infusion and integrated control of HVAC/lighting/utility systems in large multi-use buildings. The method is described in detail and applied to a modified Cerebellar Model Arithmetic Computer (CMAC) neural network regulator for systems with unknown time delays. The design and testing problem is viewed as a game, in that the controller is chosen with a minimax criterion i.e., minimize the loss associated with its use on the worst possible plant. The technique permits analysis of neural strategies against a set of feasible plants. This yields both the best choice of control parameters and identification of that plant which is most difficult for the best controller to handle.

  17. A radial basis function neural network based on artificial immune systems for remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Yan, Qin; Zhong, Yanfei

    2008-12-01

    The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre

  18. A novel technology for fabricating customizable VLSI artificial neural network chips

    SciTech Connect

    Fu, C.Y.; Law, B.; Chapline, G.; Swenson, D.

    1992-02-05

    This paper describes an implementation of hardware neural networks using highly linear thin-film resistor technology and an 8-bit binary weight circuit to produce customizable artificial neural network chips and systems. These neural networks are programmed using precision laser cutting and deposition. The fast turnaround of laser-based customization allows us to explore different neural network architectures and to rapidly program the synaptic weights. Our customizable chip allows us to expand an artificial network laterally and vertically. This flexibility permits us to build very large neural network systems.

  19. Time series predictions with neural nets: Application to airborne pollen forecasting

    NASA Astrophysics Data System (ADS)

    Arizmendi, C. M.; Sanchez, J. R.; Ramos, N. E.; Ramos, G. I.

    1993-09-01

    Pollen allergy is a common disease causing rhinoconjunctivitis (hay fever) in 5 10% of the population. Medical studies have indicated that pollen related diseases could be highly reduced if future pollen contents in the air could be predicted. In this work we have developed a new forecasting method that applies the ability of neural nets to predict the future behaviour of chaotic systems in order to make accurate predictions of the airborne pollen concentration. The method requires that the neural net be fed with non-zero values, which restricts the method predictions to the period following the start of pollen flight. The operational method outlined here constitutes a different point of view with respect to the more generally used forecasts of time series analysis, which require input of many meteorological parameters. Excellent forecasts were obtained training a neural net by using only the time series pollen concentration values.

  20. Didactic Strategy Discussion Based on Artificial Neural Networks Results.

    NASA Astrophysics Data System (ADS)

    Andina, D.; Bermúdez-Valbuena, R.

    2009-04-01

    Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.

  1. Application of an artificial neural network to pump card diagnosis

    SciTech Connect

    Ashenayi, K. ); Lea, J.F. ); Kemp, F. , Dallas, TX ); Nazi, G.A.

    1994-12-01

    Beam pumping is the most frequently used artificial-lift technique for oil production. Downhole pump cards are used to evaluate performance of the pumping unit. Pump cards can be generated from surface dynamometer cards using a 1D wave equation with viscous damping, as suggested by Gibbs and Neely. Pump cards contain significant information describing the behavior of the pump. However, interpretation of these cards is tedious and time-consuming; hence, an automated system capable of interpreting these cards could speed interpretation and warn of pump failures. This work presents the results of a DOS-based computer program capable of correctly classifying pump cards. The program uses a hybrid artificial neural network (ANN) to identify significant features of the pump card. The hybrid ANN uses classical and sinusoidal perceptrons. The network is trained using an error-back-propagation technique. The program correctly identified pump problems for more than 180 different training and test pump cards. The ANN takes a total of 80 data points as input. Sixty data points are collected from the pump card perimeter, and the remaining 20 data points represent the slope at selected points on the pump card perimeter. Pump problem conditions are grouped into 11 distinct classes. The network is capable of identifying one or more of these problem conditions for each pump card. Eight examples are presented and discussed.

  2. Training Spiking Neural Models Using Artificial Bee Colony

    PubMed Central

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  3. Artificial Neural Network-Based System for PET Volume Segmentation.

    PubMed

    Sharif, Mhd Saeed; Abbod, Maysam; Amira, Abbes; Zaidi, Habib

    2010-01-01

    Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  4. Artificial Neural Network-Based System for PET Volume Segmentation

    PubMed Central

    Sharif, Mhd Saeed; Abbod, Maysam; Amira, Abbes; Zaidi, Habib

    2010-01-01

    Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results. PMID:20936152

  5. Reliability and risk analysis using artificial neural networks

    SciTech Connect

    Robinson, D.G.

    1995-12-31

    This paper discusses preliminary research at Sandia National Laboratories into the application of artificial neural networks for reliability and risk analysis. The goal of this effort is to develop a reliability based methodology that captures the complex relationship between uncertainty in material properties and manufacturing processes and the resulting uncertainty in life prediction estimates. The inputs to the neural network model are probability density functions describing system characteristics and the output is a statistical description of system performance. The most recent application of this methodology involves the comparison of various low-residue, lead-free soldering processes with the desire to minimize the associated waste streams with no reduction in product reliability. Model inputs include statistical descriptions of various material properties such as the coefficients of thermal expansion of solder and substrate. Consideration is also given to stochastic variation in the operational environment to which the electronic components might be exposed. Model output includes a probabilistic characterization of the fatigue life of the surface mounted component.

  6. Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

    PubMed Central

    Simões, Priscyla W; Izumi, Narjara B; Casagrande, Ramon S; Venson, Ramon; Veronezi, Carlos D; Moretti, Gustavo P; da Rocha, Edroaldo L; Cechinel, Cristian; Ceretta, Luciane B; Comunello, Eros; Martins, Paulo J; Casagrande, Rogério A; Snoeyer, Maria L; Manenti, Sandra A

    2014-01-01

    OBJECTIVE To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. RESULTS After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. CONCLUSION Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study. PMID:25374454

  7. Classification of images acquired with colposcopy using artificial neural networks.

    PubMed

    Simões, Priscyla W; Izumi, Narjara B; Casagrande, Ramon S; Venson, Ramon; Veronezi, Carlos D; Moretti, Gustavo P; da Rocha, Edroaldo L; Cechinel, Cristian; Ceretta, Luciane B; Comunello, Eros; Martins, Paulo J; Casagrande, Rogério A; Snoeyer, Maria L; Manenti, Sandra A

    2014-01-01

    To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.

  8. Non-Lipschitzian dynamics for neural net modelling

    NASA Technical Reports Server (NTRS)

    Zak, Michail

    1989-01-01

    Failure of the Lipschitz condition in unstable equilibrium points of dynamical systems leads to a multiple-choice response to an initial deterministic input. The evolution of such systems is characterized by a special type of unpredictability measured by unbounded Liapunov exponents. Possible relation of these systems to future neural networks is discussed.

  9. 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.

  10. A neural net based architecture for the segmentation of mixed gray-level and binary pictures

    NASA Technical Reports Server (NTRS)

    Tabatabai, Ali; Troudet, Terry P.

    1991-01-01

    A neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.

  11. Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis

    SciTech Connect

    Athanassopoulos, S.; Mavrommatis, E.; Gernoth, K. A.; Clark, J. W.

    2006-04-26

    We construct a neural network model that predicts the differences between the experimental mass-excess values {delta}Mexp and the theoretical values {delta}MFRDM given by the Finite Range Droplet Model of Moeller et al. This difficult study reveals that subtle regularities of nuclear structure not yet embodied in the best microscopic/phenomenological models of atomic-mass systematics do actually exist. By combining the FRDM and the above neural network model we construct a Hybrid Model with improved predictive performance in the majority of the calculations of the systematics of nuclear mass excess and of related quantities. Such systematics is of current interest among others in such astrophysical problems as nucleosynthesis processes and the justification of the present abundances.

  12. A neural net-based approach to software metrics

    NASA Technical Reports Server (NTRS)

    Boetticher, G.; Srinivas, Kankanahalli; Eichmann, David A.

    1992-01-01

    Software metrics provide an effective method for characterizing software. Metrics have traditionally been composed through the definition of an equation. This approach is limited by the fact that all the interrelationships among all the parameters be fully understood. This paper explores an alternative, neural network approach to modeling metrics. Experiments performed on two widely accepted metrics, McCabe and Halstead, indicate that the approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics.

  13. A neutron spectrum unfolding code based on generalized regression artificial neural networks.

    PubMed

    Del Rosario Martinez-Blanco, Ma; Ornelas-Vargas, Gerardo; Castañeda-Miranda, Celina Lizeth; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, Jose M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel

    2016-11-01

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a (6)LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    1999-01-01

    Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

  15. 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.

  16. Neural net selection methods for Gabor transform detection filters

    NASA Astrophysics Data System (ADS)

    Casasent, David P.; Smokelin, John S.

    1993-08-01

    New Gabor transform (GT) filters to detect candidate object locations independent of the object class, object distortions, and for low contrast objects in clutter are described. A new neural network (NN) technique is described to automate selection of GT parameters and to combine multiple Gabor functions (GFs) into once composite macro GF detection filter. Fusion of real and imaginary GT filter outputs is used to reduce false alarms, (PFA), while maintaining high detection rates (PD). Test results on the TRIM-2 database are provided.

  17. International experience on the use of artificial neural networks in gastroenterology.

    PubMed

    Grossi, E; Mancini, A; Buscema, M

    2007-03-01

    In this paper, we reconsider the scientific background for the use of artificial intelligence tools in medicine. A review of some recent significant papers shows that artificial neural networks, the more advanced and effective artificial intelligence technique, can improve the classification accuracy and survival prediction of a number of gastrointestinal diseases. We discuss the 'added value' the use of artificial neural networks-based tools can bring in the field of gastroenterology, both at research and clinical application level, when compared with traditional statistical or clinical-pathological methods.

  18. Sensitivity and Calibration of Non-Destructive Evaluation Method That Uses Neural-Net Processing of Characteristic Fringe Patterns

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Weiland, Kenneth E.

    2003-01-01

    This paper answers some performance and calibration questions about a non-destructive-evaluation (NDE) procedure that uses artificial neural networks to detect structural damage or other changes from sub-sampled characteristic patterns. The method shows increasing sensitivity as the number of sub-samples increases from 108 to 6912. The sensitivity of this robust NDE method is not affected by noisy excitations of the first vibration mode. A calibration procedure is proposed and demonstrated where the output of a trained net can be correlated with the outputs of the point sensors used for vibration testing. The calibration procedure is based on controlled changes of fastener torques. A heterodyne interferometer is used as a displacement sensor for a demonstration of the challenges to be handled in using standard point sensors for calibration.

  19. Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

    PubMed Central

    Eskinazi, Ilan; Fregly, Benjamin J.

    2016-01-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

  20. Surrogate modeling of deformable joint contact using artificial neural networks.

    PubMed

    Eskinazi, Ilan; Fregly, Benjamin J

    2015-09-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models.

  1. A comparison between criterion functions for linear classifiers, with an application to neural nets

    SciTech Connect

    Barnard, E.; Casasent, D. )

    1989-09-01

    A variety of criterion functions (or scalar performance measures) have been suggested for the design of nonparametric linear classifiers. The classification performance of the most important of these on a typical two-class problem are investigated. The results of the investigation are then applied to the analysis and synthesis of neural-net classifiers.classifiers.

  2. A hybrid architecture for the implementation of the Athena neural net model

    NASA Technical Reports Server (NTRS)

    Koutsougeras, C.; Papachristou, C.

    1989-01-01

    The implementation of an earlier introduced neural net model for pattern classification is considered. Data flow principles are employed in the development of a machine that efficiently implements the model and can be useful for real time classification tasks. Further enhancement with optical computing structures is also considered.

  3. Neural net classification of x-ray pistachio nut data

    NASA Astrophysics Data System (ADS)

    Casasent, David P.; Sipe, Michael A.; Schatzki, Thomas F.; Keagy, Pamela M.; Le, Lan Chau

    1996-12-01

    Classification results for agricultural products are presented using a new neural network. This neural network inherently produces higher-order decision surfaces. It achieves this with fewer hidden layer neurons than other classifiers require. This gives better generalization. It uses new techniques to select the number of hidden layer neurons and adaptive algorithms that avoid other such ad hoc parameter selection problems; it allows selection of the best classifier parameters without the need to analyze the test set results. The agriculture case study considered is the inspection and classification of pistachio nuts using x- ray imagery. Present inspection techniques cannot provide good rejection of worm damaged nuts without rejecting too many good nuts. X-ray imagery has the potential to provide 100% inspection of such agricultural products in real time. Only preliminary results are presented, but these indicate the potential to reduce major defects to 2% of the crop with 1% of good nuts rejected. Future image processing techniques that should provide better features to improve performance and allow inspection of a larger variety of nuts are noted. These techniques and variations of them have uses in a number of other agricultural product inspection problems.

  4. A new neural net approach to robot 3D perception and visuo-motor coordination

    NASA Technical Reports Server (NTRS)

    Lee, Sukhan

    1992-01-01

    A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

  5. A new neural net approach to robot 3D perception and visuo-motor coordination

    NASA Technical Reports Server (NTRS)

    Lee, Sukhan

    1992-01-01

    A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

  6. 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.

  7. 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.

  8. 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.

  9. Prediction of problematic wine fermentations using artificial neural networks.

    PubMed

    Román, R César; Hernández, O Gonzalo; Urtubia, U Alejandra

    2011-11-01

    Artificial neural networks (ANNs) have been used for the recognition of non-linear patterns, a characteristic of bioprocesses like wine production. In this work, ANNs were tested to predict problems of wine fermentation. A database of about 20,000 data from industrial fermentations of Cabernet Sauvignon and 33 variables was used. Two different ways of inputting data into the model were studied, by points and by fermentation. Additionally, different sub-cases were studied by varying the predictor variables (total sugar, alcohol, glycerol, density, organic acids and nitrogen compounds) and the time of fermentation (72, 96 and 256 h). The input of data by fermentations gave better results than the input of data by points. In fact, it was possible to predict 100% of normal and problematic fermentations using three predictor variables: sugars, density and alcohol at 72 h (3 days). Overall, ANNs were capable of obtaining 80% of prediction using only one predictor variable at 72 h; however, it is recommended to add more fermentations to confirm this promising result.

  10. An artificial neural network predictor for tropospheric surface duct phenomena

    NASA Astrophysics Data System (ADS)

    Isaakidis, S. A.; Dimou, I. N.; Xenos, T. D.; Dris, N. A.

    2007-09-01

    In this work, an artificial neural network (ANN) model is developed and used to predict the presence of ducting phenomena for a specific time, taking into account ground values of atmospheric pressure, relative humidity and temperature. A feed forward backpropagation ANN is implemented, which is trained, validated and tested using atmospheric radiosonde data from the Helliniko airport, for the period from 1991 to 2004. The network's quality and generality is assessed using the Area Under the Receiver Operating Characteristics (ROC) Curves (AUC), which resulted to a mean value of about 0.86 to 0.90, depending on the observation time. In order to validate the ANN results and to evaluate any further improvement options of the proposed method, the problem was additionally treated using Least Squares Support Vector Machine (LS-SVM) classifiers, trained and tested with identical data sets for direct performance comparison with the ANN. Furthermore, time series prediction and the effect of surface wind to the presence of tropospheric ducts appearance are discussed. The results show that the ANN model presented here performs efficiently and gives successful tropospheric ducts predictions.

  11. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

    PubMed

    Garro, Beatriz A; Vázquez, Roberto A

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

  12. Automatic classification of DMSA scans using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

    2014-04-01

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

  13. Honey characterization using computer vision system and artificial neural networks.

    PubMed

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Signal processing using artificial neural network for BOTDA sensor system.

    PubMed

    Azad, Abul Kalam; Wang, Liang; Guo, Nan; Tam, Hwa-Yaw; Lu, Chao

    2016-03-21

    We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.

  15. A New Data Mining Scheme Using Artificial Neural Networks

    PubMed Central

    Kamruzzaman, S. M.; Jehad Sarkar, A. M.

    2011-01-01

    Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems. PMID:22163866

  16. Appraisal of artificial neural network for forecasting of economic parameters

    NASA Astrophysics Data System (ADS)

    Kordanuli, Bojana; Barjaktarović, Lidija; Jeremić, Ljiljana; Alizamir, Meysam

    2017-01-01

    The main aim of this research is to develop and apply artificial neural network (ANN) with extreme learning machine (ELM) and back propagation (BP) to forecast gross domestic product (GDP) and Hirschman-Herfindahl Index (HHI). GDP could be developed based on combination of different factors. In this investigation GDP forecasting based on the agriculture and industry added value in gross domestic product (GDP) was analysed separately. Other inputs are final consumption expenditure of general government, gross fixed capital formation (investments) and fertility rate. The relation between product market competition and corporate investment is contentious. On one hand, the relation can be positive, but on the other hand, the relation can be negative. Several methods have been proposed to monitor market power for the purpose of developing procedures to mitigate or eliminate the effects. The most widely used methods are based on indices such as the Hirschman-Herfindahl Index (HHI). The reliability of the ANN models were accessed based on simulation results and using several statistical indicators. Based upon simulation results, it was presented that ELM shows better performances than BP learning algorithm in applications of GDP and HHI forecasting.

  17. Predicting concrete corrosion of sewers using artificial neural network.

    PubMed

    Jiang, Guangming; Keller, Jurg; Bond, Philip L; Yuan, Zhiguo

    2016-04-01

    Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers.

  18. Artificial neural network algorithm for analysis of Rutherford backscattering data

    NASA Astrophysics Data System (ADS)

    Barradas, N. P.; Vieira, A.

    2000-10-01

    Rutherford backscattering (RBS) is a nondestructive, fully quantitative technique for accurately determining the compositional depth profile of thin films. The inverse RBS problem, which is to determine from the data the corresponding sample structure, is, however, in general ill posed. Skilled analysts use their knowledge and experience to recognize recurring features in the data and relate them to features in the sample structure. This is then followed by a detailed quantitative analysis. We have developed an artificial neural network (ANN) for the same purpose, applied to the specific case of Ge-implanted Si. The ANN was trained with thousands of constructed spectra of samples for which the structure is known. It thus learns how to interpret the spectrum of a given sample, without any knowledge of the physics involved. The ANN was then applied to experimental data from samples of unknown structure. The quantitative results obtained were compared with those given by traditional analysis methods and are excellent. The major advantage of ANNs over those other methods is that, after the time-consuming training phase, the analysis is instantaneous, which opens the door to automated on-line data analysis. Furthermore, the ANN was able to distinguish two different classes of data which are experimentally difficult to analyze. This opens the door to automated on-line optimization of the experimental conditions.

  19. Multiobjective analysis of a public wellfield using artificial neural networks.

    PubMed

    Coppola, Emery A; Szidarovszky, Ferenc; Davis, Donald; Spayd, Steven; Poulton, Mary M; Roman, Eric

    2007-01-01

    As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods.

  20. Sensor calibration and compensation using artificial neural network.

    PubMed

    Khan, Shakeb A; Shahani, D T; Agarwala, A K

    2003-07-01

    Artificial neural network (ANN) based inverse modeling technique is used for sensor response linearization. The choice of the order of the model and the number of the calibration points are important design parameters in this technique. An intensive study of the effect of the order of the model and number of calibration points on the lowest asymptotic root-mean-square (RMS) error has been reported in this paper. Starting from the initial value of the nonlinearity in the characteristics of a sensor and required RMS error, it is possible to quickly fix the order of the model and the number of calibration points required using results of this paper. The number of epochs needed to calibrate the sensor, and thereafter the epochs needed to recalibrate in event of sensitivity or offset drifts, are also presented to bring out the convergence time of the technique. More importantly, the advantages of the ANN technique over traditional regression based modeling are also discussed from the point of view of its advantage in hardware simplicity in microcontroller based implementation. Results presented in this paper would be of interest to instrumentation design engineers.

  1. Classification of breast abnormalities using artificial neural network

    NASA Astrophysics Data System (ADS)

    Zaman, Nur Atiqah Kamarul; Rahman, Wan Eny Zarina Wan Abdul; Jumaat, Abdul Kadir; Yasiran, Siti Salmah

    2015-05-01

    Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

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

    NASA Astrophysics Data System (ADS)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-10-01

    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.

  3. Rapid Life-Cycle Impact Screening Using Artificial Neural Networks.

    PubMed

    Song, Runsheng; Keller, Arturo A; Suh, Sangwon

    2017-08-30

    The number of chemicals in the market is rapidly increasing, while our understanding of the life-cycle impacts of these chemicals lags considerably. To address this, we developed deep artificial neural network (ANN) models to estimate life-cycle impacts of chemicals. Using molecular structure information, we trained multilayer ANNs for life-cycle impacts of chemicals using six impact categories, including cumulative energy demand, global warming (IPCC 2007), acidification (TRACI), human health (Impact2000+), ecosystem quality (Impact2000+), and eco-indicator 99 (I,I, total). The application domain (AD) of the model was estimated for each impact category within which the model exhibits higher reliability. We also tested three approaches for selecting molecular descriptors and identified the principal component analysis (PCA) as the best approach. The predictions for acidification, human health, and the eco-indicator 99 model showed relatively higher performance with R(2) values of 0.73, 0.71, and 0.87, respectively, while the global warming model had a lower R(2) of 0.48. This study indicates that ANN models can serve as an initial screening tool for estimating life-cycle impacts of chemicals for certain impact categories in the absence of more reliable information. Our analysis also highlights the importance of understanding ADs for interpreting the ANN results.

  4. Application of artificial neural network in medical geochemistry.

    PubMed

    Fajčíková, K; Stehlíková, B; Cvečková, V; Rapant, S

    2017-03-28

    For the evaluation of various adverse health effects of chemical elements occurring in the environment on humans, the comparison and linking of geochemical data (chemical composition of groundwater, soils, and dusts) with data on health status of population (so-called health indicators) play a key role. Geochemical and health data are predominantly nonlinear, and the use of standard statistical methods can lead to wrong conclusions. For linking such data, we find appropriate the use method of artificial neural networks (ANNs) which enable to eliminate data inhomogeneity and also potential data errors. Through method of ANNs, we are able to determine the order of influence of chemical elements on health indicators as well as to define limit values for the influential elements at which the health status of population is the most favourable (i.e. the lowest mortality, the highest life expectancy). For determination of dependence between the groundwater contents of chemical elements and health indicators, we recommend to create 200 ANNs. In further calculations performed for identification of order of influence of chemical elements as well as definition of limit values, we propose to work with median or mean values from calculated 200 ANNs. The ANN represents an appropriate method to be used for environmental and health data analysis in medical geochemistry.

  5. Automated isotope identification algorithm using artificial neural networks

    DOE PAGES

    Kamuda, Mark; Stinnett, Jacob; Sullivan, Clair

    2017-04-12

    There is a need to develop an algorithm that can determine the relative activities of radio-isotopes in a large dataset of low-resolution gamma-ray spectra that contains a mixture of many radio-isotopes. Low-resolution gamma-ray spectra that contain mixtures of radio-isotopes often exhibit feature over-lap, requiring algorithms that can analyze these features when overlap occurs. While machine learning and pattern recognition algorithms have shown promise for the problem of radio-isotope identification, their ability to identify and quantify mixtures of radio-isotopes has not been studied. Because machine learning algorithms use abstract features of the spectrum, such as the shape of overlapping peaks andmore » Compton continuum, they are a natural choice for analyzing radio-isotope mixtures. An artificial neural network (ANN) has be trained to calculate the relative activities of 32 radio-isotopes in a spectrum. Furthermore, the ANN is trained with simulated gamma-ray spectra, allowing easy expansion of the library of target radio-isotopes. In this paper we present our initial algorithms based on an ANN and evaluate them against a series measured and simulated spectra.« less

  6. Design The Cervical Cancer Detector Use The Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Intan Af'idah, Dwi; Didik Widianto, Eko; Setyawan, Budi

    2013-06-01

    Cancer is one of the contagious diseases that become a public health issue, both in the world and in Indonesia. In the world, 12% of all deaths caused by cancer and is the second killer after cardiovascular disease. Early detection using the IVA is a practical and inexpensive (only requiring acetic acid). However, the accuracy of the method is quite low, as it can not detect the stage of the cancer. While other methods have a better sensitivity than the IVA method, is a method of PAP smear. However, this method is relatively expensive, and requires an experienced pathologist-cytologist. According to the case above, Considered important to make the cancer cervics detector that is used to detect the abnormality and cervical cancer stage and consists of a digital microscope, as well as a computer application based on artificial neural network. The use of cervical cancer detector software and hardware are integrated each other. After the specifications met, the steps to design the cervical cancer detection are: Modifying a conventional microscope by adding a lens, image recording, and the lights, Programming the tools, designing computer applications, Programming features abnormality detection and staging of cancer.

  7. Artificial neural network model for earthquake prediction with radon monitoring.

    PubMed

    Külahci, Fatih; Inceöz, Murat; Doğru, Mahmut; Aksoy, Ercan; Baykara, Oktay

    2009-01-01

    Apart from the linear monitoring studies concerning the relationship between radon and earthquake, an artificial neural networks (ANNs) model approach is presented starting out from non-linear changes of the eight different parameters during the earthquake occurrence. A three-layer Levenberg-Marquardt feedforward learning algorithm is used to model the earthquake prediction process in the East Anatolian Fault System (EAFS). The proposed ANN system employs individual training strategy with fixed-weight and supervised models leading to estimations. The average relative error between the magnitudes of the earthquakes acquired by ANN and measured data is about 2.3%. The relative error between the test and earthquake data varies between 0% and 12%. In addition, the factor analysis was applied on all data and the model output values to see the statistical variation. The total variance of 80.18% was explained with four factors by this analysis. Consequently, it can be concluded that ANN approach is a potential alternative to other models with complex mathematical operations.

  8. Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making

    PubMed Central

    Burnside, Elizabeth S.

    2013-01-01

    Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs), in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions. PMID:23781276

  9. Synoptic Classification and Establishment of Analogues with Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Michaelides, S. C.; Liassidou, F.; Schizas, C. N.

    2007-06-01

    Weather charts depicting the spatial distribution of various meteorological parameters constitute an indispensable pictorial tool for meteorologists, in diagnosing and forecasting synoptic conditions and the associated weather. The purpose of the present research is to investigate whether training artificial neural networks can be employed in the objective identification of synoptic patterns on weather charts. In order to achieve this, the daily analyses at 0000UTC for 1996 were employed. The respective data consist of the grid-point values of the geopotential height of the 500 hPa isobaric level in the atmosphere. A uniform grid-point spacing of 2.5° × 2.5° is used and the geographical area covered by the investigation lies between 25°N and 65°N and between 20°W and 50°E, covering Europe, the Middle East and the Northern African Coast. An unsupervised learning self-organizing feature map algorithm, namely the Kohonen's algorithm, was employed. The input consists of the grid-point data described above and the output is the synoptic class which each day belongs to. The results referred to in this study employ the generation of 15 and 20 synoptic classes (more classes have been investigated but the results are not reported here). The results indicate that the present technique produced a satisfactory classification of the synoptic patterns over the geographical region mentioned above. Also, it is revealed that the classification performed in this study exhibits a strong seasonal relationship.

  10. Using artificial neural network tools to analyze microbial biomarker data

    SciTech Connect

    Brandt, C.C.; Schryver, J.C.; Almeida, J.S.; Pfiffner, S.M.; Palumbo, A.V.

    2004-03-17

    A major challenge in the successful implementation of bioremediation is understanding the structure of the indigenous microbial community and how this structure is affected by environmental conditions. Culture-independent approaches that use biomolecular markers have become the key to comparative microbial community analysis. However, the analysis of biomarkers from environmental samples typically generates a large number of measurements. The large number and complex nonlinear relationships among these measurements makes conventional linear statistical analysis of the data difficult. New data analysis tools are needed to help understand these data. We adapted artificial neural network (ANN) tools for relating changes in microbial biomarkers to geochemistry. ANNs are nonlinear pattern recognition methods that can learn from experience to improve their performance. We have successfully applied these techniques to the analysis of membrane lipids and nucleic acid biomarker data from both laboratory and field studies. Although ANNs typically outperform linear data analysis techniques, the user must be aware of several considerations and issues to ensure that analysis results are not misleading: (1) Overfitting, especially in small sample size data sets; (2) Model selection; (3) Interpretation of analysis results; and (4) Availability of tools (code). This poster summarizes approaches for addressing each of these issues. The objectives are: (1) Develop new nonlinear data analysis tools for relating microbial biomolecular markers to geochemical conditions; (2) Apply these nonlinear tools to field and laboratory studies relevant to the NABIR Program; and (3) Provide these tools and guidance in their use to other researchers.

  11. Use of artificial neural network for spatial rainfall analysis

    NASA Astrophysics Data System (ADS)

    Paraskevas, Tsangaratos; Dimitrios, Rozos; Andreas, Benardos

    2014-04-01

    In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.

  12. Quantum-based algorithm for optimizing artificial neural networks.

    PubMed

    Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang

    2013-08-01

    This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.

  13. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

    PubMed Central

    Vázquez, Roberto A.

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

  14. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

    PubMed Central

    Lai, Jinxing

    2016-01-01

    In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability. PMID:26819587

  15. Pharyngeal wall vibration detection using an artificial neural network.

    PubMed

    Behbehani, K; Lopez, F; Yen, F C; Lucas, E A; Burk, J R; Axe, J P; Kamangar, F

    1997-05-01

    An artificial-neural-network-based detector of pharyngeal wall vibration (PWV) is presented. PWV signals the imminent occurrence of obstructive sleep apnoea (OSA) in adults who suffer from OSA syndrome. Automated detection of PWV is very important in enhancing continuous positive airway pressure (CPAP) therapy by allowing automatic adjustment of the applied airway pressure by a procedure called automatic positive airway pressure (APAP) therapy. A network with 15 inputs, one output, and two hidden layers, each with two Adaline-nodes, is used as part of a PWV detection scheme. The network is initially trained using nasal mask pressure data from five positively diagnosed OSA patients. The performance of the ANN-based detector is evaluated using data from five different OSA patients. The results show that on the average it correctly detects the presence of PWV events at a rate of approximately 92% and correctly distinguishes normal breaths approximately 98% of the time. Further, the ANN-based detector accuracy is not affected by the pressure level required for therapy.

  16. Automatic classification of DMSA scans using an artificial neural network.

    PubMed

    Wright, J W; Duguid, R; McKiddie, F; Staff, R T

    2014-04-07

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from 'definitely normal' to 'definitely abnormal'. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

  17. Geochemical characterization of oceanic basalts using Artificial Neural Network.

    PubMed

    Das, Pranab; Iyer, Sridhar D

    2009-12-23

    The geochemical discriminate diagrams help to distinguish the volcanics recovered from different tectonic settings but these diagrams tend to group the ocean floor basalts (OFB) under one class i.e., as mid-oceanic ridge basalts (MORB). Hence, a method is specifically needed to identify the OFB as normal (N-MORB), enriched (E-MORB) and ocean island basalts (OIB). We have applied Artificial Neural Network (ANN) technique as a supervised Learning Vector Quantisation (LVQ) to identify the inherent geochemical signatures present in the Central Indian Ocean Basin (CIOB) basalts. A range of N-MORB, E-MORB and OIB dataset was used for training and testing of the network. Although the identification of the characters as N-MORB, E-MORB and OIB is completely dependent upon the training data set for the LVQ, but to a significant extent this method is found to be successful in identifying the characters within the CIOB basalts. The study helped to geochemically delineate the CIOB basalts as N-MORB with perceptible imprints of E-MORB and OIB characteristics in the form of moderately enriched rare earth and incompatible elements. Apart from the fact that the magmatic processes are difficult to be deciphered, the architecture performs satisfactorily.

  18. Superiority of artificial neural networks for a genetic classification procedure.

    PubMed

    Sant'Anna, I C; Tomaz, R S; Silva, G N; Nascimento, M; Bhering, L L; Cruz, C D

    2015-08-19

    The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

  19. Multiobjective analysis of a public wellfield using artificial neural networks

    USGS Publications Warehouse

    Coppola, E.A.; Szidarovszky, F.; Davis, D.; Spayd, S.; Poulton, M.M.; Roman, E.

    2007-01-01

    As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods. ?? 2006 National Ground Water Association.

  20. Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

    NASA Astrophysics Data System (ADS)

    Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui

    2016-11-01

    Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.

  1. Novel Screening Tool for Stroke Using Artificial Neural Network.

    PubMed

    Abedi, Vida; Goyal, Nitin; Tsivgoulis, Georgios; Hosseinichimeh, Niyousha; Hontecillas, Raquel; Bassaganya-Riera, Josep; Elijovich, Lucas; Metter, Jeffrey E; Alexandrov, Anne W; Liebeskind, David S; Alexandrov, Andrei V; Zand, Ramin

    2017-06-01

    The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting. Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model. We developed an artificial neural network (ANN) model. The learning algorithm was based on backpropagation. To validate the model, we used a 10-fold cross-validation method. A total of 260 patients (equal number of stroke mimics and ACIs) were enrolled for the development and validation of our ANN model. Our analysis indicated that the average sensitivity and specificity of ANN for the diagnosis of ACI based on the 10-fold cross-validation analysis was 80.0% (95% confidence interval, 71.8-86.3) and 86.2% (95% confidence interval, 78.7-91.4), respectively. The median precision of ANN for the diagnosis of ACI was 92% (95% confidence interval, 88.7-95.3). Our results show that ANN can be an effective tool for the recognition of ACI and differentiation of ACI from stroke mimics at the initial examination. © 2017 American Heart Association, Inc.

  2. A new data mining scheme using artificial neural networks.

    PubMed

    Kamruzzaman, S M; Jehad Sarkar, A M

    2011-01-01

    Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems.

  3. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

    DTIC Science & Technology

    1987-10-01

    This was an instrumentation grant to purchase equipment of support of research in neural networks, information science , artificial intelligence, and applied mathematics. Computer lab equipment, motor control and robotics lab equipment, speech analysis equipment and computational vision equipment were purchased.

  4. Artificial Neural Networks Applications: from Aircraft Design Optimization to Orbiting Spacecraft On-board Environment Monitoring

    NASA Technical Reports Server (NTRS)

    Jules, Kenol; Lin, Paul P.

    2002-01-01

    This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

  5. Confidence intervals in Flow Forecasting by using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Panagoulia, Dionysia; Tsekouras, George

    2014-05-01

    One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input

  6. Automatic classification of transiently evoked otoacoustic emissions using an artificial neural network.

    PubMed

    Buller, G; Lutman, M E

    1998-08-01

    The increasing use of transiently evoked otoacoustic emissions (TEOAE) in large neonatal hearing screening programmes makes a standardized method of response classification desirable. Until now methods have been either subjective or based on arbitrary response characteristics. This study takes an expert system approach to standardize the subjective judgements of an experienced scorer. The method that is developed comprises three stages. First, it transforms TEOAEs from waveforms in the time domain into a simplified parameter set. Second, the parameter set is classified by an artificial neural network that has been taught on a large database TEOAE waveforms and corresponding expert scores. Third, additional fuzzy logic rules automatically detect probable artefacts in the waveforms and synchronized spontaneous emission components. In this way, the knowledge of the experienced scorer is encapsulated in the expert system software and thereafter can be accessed by non-experts. Teaching and evaluation of the neural network was based on TEOAEs from a database totalling 2190 neonatal hearing screening tests. The database was divided into learning and test groups with 820 and 1370 waveforms respectively. From each recorded waveform a set of 12 parameters was calculated, representing signal static and dynamic properties. The artifical network was taught with parameter sets of only the learning groups. Reproduction of the human scorer classification by the neural net in the learning group showed a sensitivity for detecting screen fails of 99.3% (299 from 301 failed results on subjective scoring) and a specificity for detecting screen passes of 81.1% (421 of 519 pass results). To quantify the post hoc performance of the net (generalization), the test group was then presented to the network input. Sensitivity was 99.4% (474 from 477) and specificity was 87.3% (780 from 893). To check the efficiency of the classification method, a second learning group was selected out of the

  7. Research on architecture of intelligent design platform for artificial neural network expert system

    NASA Astrophysics Data System (ADS)

    Gu, Honghong

    2017-09-01

    Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

  8. An artificial neural network system for diagnosing gas turbine engine fuel faults

    SciTech Connect

    Illi, O.J. Jr.; Greitzer, F.L.; Kangas, L.J.; Reeve, T.

    1994-04-01

    The US Army Ordnance Center & School and Pacific Northwest Laboratories are developing a turbine engine diagnostic system for the M1A1 Abrams tank. This system employs Artificial Neural Network (AN) technology to perform diagnosis and prognosis of the tank`s AGT-1500 gas turbine engine. This paper describes the design and prototype development of the ANN component of the diagnostic system, which we refer to as ``TEDANN`` for Turbine Engine Diagnostic Artificial Neural Networks.

  9. Casting a Wide Net: Role of Perineuronal Nets in Neural Plasticity

    PubMed Central

    Berretta, Sabina; Blacktop, Jordan M.; Fawcett, James W.; Kitagawa, Hiroshi; Kwok, Jessica C.F.; Miquel, Marta

    2016-01-01

    Perineuronal nets (PNNs) are unique extracellular matrix structures that wrap around certain neurons in the CNS during development and control plasticity in the adult CNS. They appear to contribute to a wide range of diseases/disorders of the brain, are involved in recovery from spinal cord injury, and are altered during aging, learning and memory, and after exposure to drugs of abuse. Here the focus is on how a major component of PNNs, chondroitin sulfate proteoglycans, control plasticity, and on the role of PNNs in memory in normal aging, in a tauopathy model of Alzheimer's disease, and in drug addiction. Also discussed is how altered extracellular matrix/PNN formation during development may produce synaptic pathology associated with schizophrenia, bipolar disorder, major depression, and autism spectrum disorders. Understanding the molecular underpinnings of how PNNs are altered in normal physiology and disease will offer insights into new treatment approaches for these diseases. PMID:27911749

  10. Neural net classification and LMS reconstruction to halftone images

    NASA Astrophysics Data System (ADS)

    Chang, Pao-Chi; Yu, Che-Sheng

    1998-01-01

    The objective of this work is to reconstruct high quality gray-level images from halftone images, or the inverse halftoning process. We develop high performance halftone reconstruction methods for several commonly used halftone techniques. For better reconstruction quality, image classification based on halftone techniques is placed before the reconstruction process so that the halftone reconstruction process can be fine tuned for each halftone technique. The classification is based on enhanced 1D correlation of halftone images and processed with a three- layer back propagation neural network. This classification method reached 100 percent accuracy with a limited set of images processed by dispersed-dot ordered dithering, clustered-dot ordered dithering, constrained average, and error diffusion methods in our experiments. For image reconstruction, we apply the least-mean-square adaptive filtering algorithm which intends to discover the optimal filter weights and the mask shapes. As a result, it yields very good reconstruction image quality. The error diffusion yields the best reconstructed quality among the halftone methods. In addition, the LMS method generates optimal image masks which are significantly different for each halftone method. These optimal masks can also be applied to more sophisticated reconstruction methods as the default filter masks.

  11. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

    PubMed

    Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

    2017-02-01

    We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Artificial Neural Networks applied to landslide susceptibility assessment

    NASA Astrophysics Data System (ADS)

    Ermini, Leonardo; Catani, Filippo; Casagli, Nicola

    2005-03-01

    Landslide hazard mapping is often performed through the identification and analysis of hillslope instability factors, usually managed as thematic data within geographic information systems (GIS). In heuristic approaches, these factors are rated by the attribution of scores based on the assumed role played by each of them in controlling the development of a sliding process. Other more refined methods, based on the principle that the present and the past are keys to the future, have also been developed, thus allowing less subjective analyses in which landslide susceptibility is assessed by statistical relationships between past landslide events and hillslope instability factors. The objective of this research is to define a method with the ability to forecast landslide susceptibility through the application of Artificial Neural Networks (ANNs). The Riomaggiore catchment, a subwatershed of the Reno River basin located in the Northern Apennines (Italy), was chosen as an ideal test site, as it is representative of many of the geomorphological settings within this region. In the present application, two different ANNs, used in classification problems, were set up and applied: one belonging to the category of Multi-Layered Perceptron (MLP) and the other to the Probabilistic Neural Network (PNN) family. The hillslope factors that have been taken into account in the analysis were the following: (a) lithology, (b) slope angle, (c), profile curvature, (d) land cover and (e) upslope contributing area. These factors have been classified on nominal scales, and their intersection allowed 3342 homogeneous domains (Unique Condition Unit, UCU) to be singled out, which correspond to the terrain units utilized in this analysis. The model vector used to train the ANNs is a subset of that derived from the production of Unique Condition Units and consists of 3342 records organized in input and output variable vectors. In particular, the hillslope factors, once classified on nominal

  13. Modeling Career Counselor Decisions with Artificial Neural Networks: Predictions of Fit across a Comprehensive Occupational Map.

    ERIC Educational Resources Information Center

    Carson, Andrew D.; Bizot, Elizabeth B.; Hendershot, Peggy E.; Barton, Margaret G.; Garvin, Mary K.; Kraemer, Barbara

    1999-01-01

    Career recommendations were made based on aptitude scores of 335 high school freshmen. Artificial neural networks were used to map recommendations to 12 occupational clusters. Overall accuracy of neural networks (.80) approached that of discriminant function analysis (.84). The two methods had different strengths and weaknesses. (SK)

  14. Artificial Neural Network Prediction of Chemical-Disease Relationships using Readily Available Chemical Properties

    DTIC Science & Technology

    2014-03-27

    Training of a Neural Network for Predicting the SOlvent Accessibility of Proteins. Journal of Computational Chemistry , 1313-1320. Ashizawa, K...8217 performance in the differential diagnosis of interstitial lung disease using chest radiographs. American Journal of Roentgenology, 172(5), 1311-1315...Khodabakhshi, R. (2012). Artificial neural network for prediction of distant metastasis in colorectal cancer. Asian Pacific Journal of Cancer

  15. Modeling Career Counselor Decisions with Artificial Neural Networks: Predictions of Fit across a Comprehensive Occupational Map.

    ERIC Educational Resources Information Center

    Carson, Andrew D.; Bizot, Elizabeth B.; Hendershot, Peggy E.; Barton, Margaret G.; Garvin, Mary K.; Kraemer, Barbara

    1999-01-01

    Career recommendations were made based on aptitude scores of 335 high school freshmen. Artificial neural networks were used to map recommendations to 12 occupational clusters. Overall accuracy of neural networks (.80) approached that of discriminant function analysis (.84). The two methods had different strengths and weaknesses. (SK)

  16. Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

    ERIC Educational Resources Information Center

    Everson, Howard T.; And Others

    This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

  17. Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

    NASA Astrophysics Data System (ADS)

    Li, Hong; Ding, Xue

    2017-03-01

    This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

  18. Simulated annealing and stochastic learning in optical neural nets: An optical Boltzmann machine

    SciTech Connect

    Shae, Zonyin.

    1989-01-01

    This dissertation deals with the study of stochastic learning and neural computation in opto-electronic hardware. It presents the first demonstration of a fully operational optical learning machine. Learning in the machine is stochastic taking place in a self-organized multi-layered opto-electronic neural net with plastic connectivity weights that are formed in a programmable non-volatile spatial light modulator. Operation of the machine is made possible by two developments in this work: (a) Fast annealing by optically induced tremors in the energy landscape of the net. The objective of this scheme is to exploit the parallelism of the optical noise pattern so as to speed up the simulated annealing process. The procedure can be viewed as that of generating controlled gradually decreasing deformations or tremors in the energy landscape of the net that prevents entrapment in a local minimum energy state. Both the random drawing of neurons and the state update of the net are now done in parallel at the same time and without having to computer explicitly the change in the energy of the net and associated Boltzmann factor as required ordinarily in the Metropolis-Kirkpartrik simulated annealing algorithm. This leads to significant acceleration of the annealing process. (b) Stochastic learning with binary weights. Learning in opto-electronic neural nets can be simplified greatly if binary weights can be used. A third development, that is the development of schemes for driving and enhancing the frame rate of magneto-optic spatial light modulators, can make the machine learning speed potentially fast. Details of these developments together with the principle, architecture, structure, and performance evaluation of this machine are given.

  19. Artificial neural network to search for metal-poor galaxies

    NASA Astrophysics Data System (ADS)

    Shi, Fei; Liu, Yu-Yan; Kong, Xu; Chen, Yang

    2014-02-01

    Aims: To find a fast and reliable method for selecting metal-poor galaxies (MPGs), especially in large surveys and huge databases, an artificial neural network (ANN) method is applied to a sample of star-forming galaxies from the Sloan Digital Sky Survey (SDSS) data release 9 (DR9) provided by the Max Planck Institute and the Johns Hopkins University (MPA/JHU). Methods: A two-step approach is adopted: (i) The ANN network must be trained with a subset of objects that are known to be either MPGs or metal rich galaxies (MRGs), treating the strong emission line flux measurements as input feature vectors in n-dimensional space, where n is the number of strong emission line flux ratios. (ii) After the network is trained on a sample of star-forming galaxies, the remaining galaxies are classified in the automatic test analysis as either MPGs or MRGs. We consider several random divisions of the data into training and testing sets; for instance, for our sample, a total of 70 percent of the data are involved in training the algorithm, 15 percent are involved in validating the algorithm, and the remaining 15 percent are used for blind testing the resulting classifier. Results: For target selection, we have achieved an acquisition rate for MPGs of 96 percent and 92 percent for an MPGs threshold of 12 + log (O/H) = 8.00 and 12 + log (O/H) = 8.39, respectively. Running the code takes minutes in most cases under the Matlab 2013a software environment. The ANN method can easily be extended to any MPGs target selection task when the physical property of the target can be expressed as a quantitative variable. The code in the paper is available on the web (http://fshi5388.blog.163.com).

  20. Artificial neural network prediction of PWR critical boron concentration

    SciTech Connect

    Wendt, S.E.; Maldonado, G.I.; Bartlett, E.B.

    1995-12-31

    The direct calculation of core parameters such as k{sub eff} and pin power peaks for light water reactors is ordinarily accomplished by numerically solving the neutron diffusion equation. Despite the rapid advances in computer architecture and algorithm development, further calculational speedups are always in great demand. One example of such an application is nuclear fuel management optimization, where the core attributes of tens of thousands of loading pattern candidates must typically be evaluated over the fuel cycle. If an artificial neural network (ANN) could be trained to accurately model the neutronic behavior of a core, a substantial time savings could be realized in the prediction of core parameters. Such an ANN could be exploited in at least two ways: 1. The a priori training of an ANN model could be tailored to address a specific plant and its corresponding licensing core neutronics software. 2. Once trained to within acceptable accuracy guidelines, an ANN model could provide the luxury of nearly instantaneous evaluations of core parameters. Recent publications by Kim et al. on core parameter prediction via ANNs have revealed a variety of promising results, which, in part, motivated our studies. Kim proved that a solution was possible; however, the large size and complexity of such a model can lead to memorization instead of generalization of the problem`s solution. Thus, the purpose of this work was to show that a much smaller ANN could predict a global core parameter such as the critical boron concentration over a wide range of training and validation data. The successful modeling of this problem with a much smaller ANN is considered to be a significant highlight of this study. This work employed Studsvik of America`s SOA1 Database, which proved to be useful for ANN training and validation.

  1. Locus minimization in breed prediction using artificial neural network approach.

    PubMed

    Iquebal, M A; Ansari, M S; Sarika; Dixit, S P; Verma, N K; Aggarwal, R A K; Jayakumar, S; Rai, A; Kumar, D

    2014-12-01

    Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible (http://nabg.iasri.res.in/bisgoat) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51,850 samples of allelic data of microsatellite-marker-based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio-piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs.

  2. Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

    NASA Astrophysics Data System (ADS)

    Bakanovskaya, L. N.

    2016-08-01

    The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

  3. tf_unet: Generic convolutional neural network U-Net implementation in Tensorflow

    NASA Astrophysics Data System (ADS)

    Akeret, Joel; Chang, Chihway; Lucchi, Aurelien; Refregier, Alexandre

    2016-11-01

    tf_unet mitigates radio frequency interference (RFI) signals in radio data using a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. The code is not tied to a specific segmentation and can be used, for example, to detect radio frequency interference (RFI) in radio astronomy or galaxies and stars in widefield imaging data. This U-Net implementation can outperform classical RFI mitigation algorithms.

  4. Neural Net-Based Redesign of Transonic Turbines for Improved Unsteady Aerodynamic Performance

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.; Rai, Man Mohan; Huber, Frank W.

    1998-01-01

    A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology (RSM) and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The optimization procedure yields a modified design that improves the aerodynamic performance through small changes to the reference design geometry. The computed results demonstrate the capabilities of the neural net-based design procedure, and also show the tremendous advantages that can be gained by including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

  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. Optimization of Training Sets For Neural-Net Processing of Characteristic Patterns From Vibrating Solids

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J. (Inventor)

    2006-01-01

    An artificial neural network is disclosed that processes holography generated characteristic pattern of vibrating structures along with finite-element models. The present invention provides for a folding operation for conditioning training sets for optimally training forward-neural networks to process characteristic fringe pattern. The folding pattern increases the sensitivity of the feed-forward network for detecting changes in the characteristic pattern The folding routine manipulates input pixels so as to be scaled according to the location in an intensity range rather than the position in the characteristic pattern.

  7. Flaws Identification Using Eddy Current Differential Transducer and Artificial Neural Networks

    SciTech Connect

    Chady, T.; Lopato, P.

    2006-03-06

    In this paper we present a multi-frequency excitation eddy current differential transducer and dynamic neural models which were used to detect and identify artificial flaws in thin conducting plates. Plates are made of Inconel600. EDM notches have relative depth from 10% to 80% and length from 2 mm to 7 mm. All flaws were located on the opposite surface of the examined specimen. Measured signals were used as input for training and verifying dynamic neural networks with a moving window. Wide range of ANN (Artificial Neural Network) structures are examined for different window length and different number of frequency components in excitation signal. Observed trends are presented in this paper.

  8. Evaluation of the efficiency of artificial neural networks for genetic value prediction.

    PubMed

    Silva, G N; Tomaz, R S; Sant'Anna, I C; Carneiro, V Q; Cruz, C D; Nascimento, M

    2016-03-28

    Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency.

  9. Calibration of a shock wave position sensor using artificial neural networks

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Weiland, Kenneth E.

    1993-01-01

    This report discusses the calibration of a shock wave position sensor. The position sensor works by using artificial neural networks to map cropped CCD frames of the shadows of the shock wave into the value of the shock wave position. This project was done as a tutorial demonstration of method and feasibility. It used a laboratory shadowgraph, nozzle, and commercial neural network package. The results were quite good, indicating that artificial neural networks can be used efficiently to automate the semi-quantitative applications of flow visualization.

  10. Forecasting jet fuel prices using artificial neural networks. Master`s thesis

    SciTech Connect

    Kasprzak, M.A.

    1995-03-01

    Artificial neural networks provide a new approach to commodity forecasting that does not require algorithm or rule development. Neural networks have been deemed successful in applications involving optimization, classification, identification, pattern recognition and time series forecasting. With the advent of user friendly, commercially available software packages that work in a spreadsheet environment, such as Neural Works Predict by NeuralWare, more people can take advantage of the power of artificial neural networks. This thesis provides an introduction to neural networks, and reviews two recent studies of forecasting commodities prices. This study also develops a neural network model using Neural Works Predict that forecasts jet fuel prices for the Defense Fuel Supply Center (DFSC). In addition, the results developed are compared to the output of an econometric regression model, specifically, the Department of Energy`s Short-Term Integrated Forecasting System (STWS) model. The Predict artificial neural network model produced more accurate results and reduced the contribution of outliers more effectively than the STIFS model, thus producing a more robust model.

  11. Latent Heat and Sensible Heat Fluxes Simulation in Maize Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Safa, B.

    2015-12-01

    flux net radiation, wind speed, air temperature, leaf area index and vapor pressure deficit. This study reveals that artificial neural networks are not only a powerful technique for estimation of LE and H fluxes, but also can identify the effectiveness of each input on the fluxes.

  12. Identification of Propionibacteria to the species level using Fourier transform infrared spectroscopy and artificial neural networks.

    PubMed

    Dziuba, B

    2013-01-01

    Fourier transform infrared spectroscopy (FTIR) and artificial neural networks (ANN's) were used to identify species of Propionibacteria strains. The aim of the study was to improve the methodology to identify species of Propionibacteria strains, in which the differentiation index D, calculated based on Pearson's correlation and cluster analyses were used to describe the correlation between the Fourier transform infrared spectra and bacteria as molecular systems brought unsatisfactory results. More advanced statistical methods of identification of the FTIR spectra with application of artificial neural networks (ANN's) were used. In this experiment, the FTIR spectra of Propionibacteria strains stored in the library were used to develop artificial neural networks for their identification. Several multilayer perceptrons (MLP) and probabilistic neural networks (PNN) were tested. The practical value of selected artificial neural networks was assessed based on identification results of spectra of 9 reference strains and 28 isolates. To verify results of isolates identification, the PCR based method with the pairs of species-specific primers was used. The use of artificial neural networks in FTIR spectral analyses as the most advanced chemometric method supported correct identification of 93% bacteria of the genus Propionibacterium to the species level.

  13. Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks.

    PubMed

    Koprowski, Robert; Lanza, Michele; Irregolare, Carlo

    2016-11-15

    Efficacy and high availability of surgery techniques for refractive defect correction increase the number of patients who undergo to this type of surgery. Regardless of that, with increasing age, more and more patients must undergo cataract surgery. Accurate evaluation of corneal power is an extremely important element affecting the precision of intraocular lens (IOL) power calculation and errors in this procedure could affect quality of life of patients and satisfaction with the service provided. The available device able to measure corneal power have been tested to be not reliable after myopic refractive surgery. Artificial neural networks with error backpropagation and one hidden layer were proposed for corneal power prediction. The article analysed the features acquired from the Pentacam HR tomograph, which was necessary to measure the corneal power. Additionally, several billion iterations of artificial neural networks were conducted for several hundred simulations of different network configurations and different features derived from the Pentacam HR. The analysis was performed on a PC with Intel(®) Xeon(®) X5680 3.33 GHz CPU in Matlab(®) Version 7.11.0.584 (R2010b) with Signal Processing Toolbox Version 7.1 (R2010b), Neural Network Toolbox 7.0 (R2010b) and Statistics Toolbox (R2010b). A total corneal power prediction error was obtained for 172 patients (113 patients forming the training set and 59 patients in the test set) with an average age of 32 ± 9.4 years, including 67% of men. The error was at an average level of 0.16 ± 0.14 diopters and its maximum value did not exceed 0.75 dioptres. The Pentacam parameters (measurement results) providing the above result are tangential anterial/posterior. The corneal net power and equivalent k-reading power. The analysis time for a single patient (a single eye) did not exceed 0.1 s, whereas the time of network training was about 3 s for 1000 iterations (the number of neurons in the hidden layer was 400).

  14. The concept models and implementations of multiport neural net associative memory for 2D patterns

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Nikolskyy, Aleksandr I.; Yatskovskaya, Rimma A.; Yatskovsky, Victor I.

    2011-04-01

    The paper considers neural net models and training and recognizing algorithms with base neurobiologic operations: p-step autoequivalence and non-equivalenc The Modified equivalently models (MEMs) of multiport neural net associative memory (MNNAM) are offered with double adaptive - equivalently weighing (DAEW) for recognition of 2D-patterns (images). It is shown, the computing process in MNNAM under using the proposed MEMs, is reduced to two-step and multi-step algorithms and step-by-step matrix-matrix (tensor-tensor) procedures. The given results of computer simulations confirmed the perspective of such models. Besides the result was received when MNNAM capacity on base of MEMs exceeded the amount of neurons.

  15. Neural net design of Gabor wavelet filters for distortion-invariant object detection in clutter

    NASA Astrophysics Data System (ADS)

    Casasent, David P.; Smokelin, John S.

    1994-03-01

    We consider the detection of multiclasses of objects in clutter with 3D object distortions and contrast differences present. We use a correlator since shift invariance is necessary to handle an object whose location is not known and to handle multiple objects. The detection filter used is a linear combination of the real part of different Gabor filters which we refer to as a macro Gabor filter (MGF). A new analysis of the parameters for the initial set of Gabor functions in the MGF is given a new neural net algorithm to refine these initial filter parameters and to determine the combination coefficients to produce the final MGF detection filter are detailed. Initial detection results are given. Use of this general neural net technique to design correlation filters seems very attractive for this and other applications.

  16. Measurement of relative density of tissue using wavelet analysis and neural nets

    NASA Astrophysics Data System (ADS)

    Suyatinov, Sergey I.; Kolentev, Sergey V.; Buldakova, Tatyana I.

    2001-01-01

    Development of methods for indirect measurement of substance's consistence and characteristics is highly actual problem of medical diagnostics. Many diseases bring about changes of tissue density or appearances of alien bodies (e.g. stones in kidneys or gallbladders). Propose to use wavelet-analysis and neural nets for indirect measurement of relative density of tissue by images of internal organs. It shall allow to reveal a disease on early stage.

  17. Detection of apnea using a short-window FFT technique and an artificial neural network

    NASA Astrophysics Data System (ADS)

    Waldemark, Karina E.; Agehed, Kenneth I.; Lindblad, Thomas; Waldemark, Joakim T. A.

    1998-03-01

    Sleep apnea is characterized by frequent prolonged interruptions of breathing during sleep. This syndrome causes severe sleep disorders and is often responsible for development of other diseases such as heart problems, high blood pressure and daytime fatigue, etc. After diagnosis, sleep apnea is often successfully treated by applying positive air pressure (CPAP) to the mouth and nose. Although effective, the (CPAP) equipment takes up a lot of space and the connected mask causes a lot of inconvenience for the patients. This raised interest in developing new techniques for treatment of sleep apnea syndrome. Several studies have indicated that electrical stimulation of the hypoglossal nerve and muscle in the tongue may be a useful method for treating patients with severe sleep apnea. In order to be able to successfully prevent the occurrence of apnea it is necessary to have some technique for early and fast on-line detection or prediction of the apnea events. This paper suggests using measurements of respiratory airflow (mouth temperature). The signal processing for this task includes the use of a short window FFT technique and uses an artificial back propagation neural net to model or predict the occurrence of apneas. The results show that early detection of respiratory interruption is possible and that the delay time for this is small.

  18. Application of Artificial Neural Network to Predict Colour Change, Shrinkage and Texture of Osmotically Dehydrated Pumpkin

    NASA Astrophysics Data System (ADS)

    Tang, S. Y.; Lee, J. S.; Loh, S. P.; Tham, H. J.

    2017-06-01

    The objectives of this study were to use Artificial Neural Network (ANN) to predict colour change, shrinkage and texture of osmotically dehydrated pumpkin slices. The effects of process variables such as concentration of osmotic solution, immersion temperature and immersion time on the above mentioned physical properties were studied. The colour of the samples was measured using a colorimeter and the net colour difference changes, ΔE were determined. The texture was measured in terms of hardness by using a Texture Analyzer. As for the shrinkage, displacement of volume method was applied and percentage of shrinkage was obtained in terms of volume changes. A feed-forward backpropagation network with sigmoidal function was developed and best network configuration was chosen based on the highest correlation coefficients between the experimental values versus predicted values. As a comparison, Response Surface Methodology (RSM) statistical analysis was also employed. The performances of both RSM and ANN modelling were evaluated based on absolute average deviation (AAD), correlation of determination (R2) and root mean square error (RMSE). The results showed that ANN has higher prediction capability as compared to RSM. The relative importance of the variables on the physical properties were also determined by using connection weight approach in ANN. It was found that solution concentration showed the highest influence on all three physical properties.

  19. Fast radiative transfer of dust reprocessing in semi-analytic models with artificial neural networks

    NASA Astrophysics Data System (ADS)

    Silva, Laura; Fontanot, Fabio; Granato, Gian Luigi

    2012-06-01

    A serious concern for semi-analytical galaxy formation models, aiming to simulate multiwavelength surveys and to thoroughly explore the model parameter space, is the extremely time-consuming numerical solution of the radiative transfer of stellar radiation through dusty media. To overcome this problem, we have implemented an artificial neural network (ANN) algorithm in the radiative transfer code GRASIL, in order to significantly speed up the computation of the infrared (IR) spectral energy distribution (SED). The ANN we have implemented is of general use, in that its input neurons are defined as those quantities effectively determining the shape of the IR SED. Therefore, the training of the ANN can be performed with any model and then applied to other models. We made a blind test to check the algorithm, by applying a net trained with a standard chemical evolution model (i.e. CHE_EVO) to a mock catalogue extracted from the semi-analytic model MORGANA, and compared galaxy counts and evolution of the luminosity functions in several near-IR to sub-millimetre (sub-mm) bands, and also the spectral differences for a large subset of randomly extracted models. The ANN is able to excellently approximate the full computation, but with a gain in CPU time by ˜2 orders of magnitude. It is only advisable that the training covers reasonably well the range of values of the input neurons in the application. Indeed in the sub-mm at high redshift, a tiny fraction of models with some sensible input neurons out of the range of the trained net cause wrong answer by the ANN. These are extreme starbursting models with high optical depths, favourably selected by sub-mm observations, and are difficult to predict a priori.

  20. The application of artificial neural networks in astronomy

    NASA Astrophysics Data System (ADS)

    Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

    2006-12-01

    Artificial Neural Networks (ANNs) are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds: supervised and unsupervised. The supervised approach lays the stress on "teaching" a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful when the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness and dynamically rapid response as well as strong capability of dealing with non-linear problems. In the last few years there has been an increasing interest toward the astronomical applications of ANNs. In this paper, the authors firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network toplogies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. They summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. Here, the focus is on some of the most interesting fields of its application, for example: object detection, star/galaxy classification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been

  1. Designing laboratory wind simulations using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Križan, Josip; Gašparac, Goran; Kozmar, Hrvoje; Antonić, Oleg; Grisogono, Branko

    2015-05-01

    While experiments in boundary layer wind tunnels remain to be a major research tool in wind engineering and environmental aerodynamics, designing the modeling hardware required for a proper atmospheric boundary layer (ABL) simulation can be costly and time consuming. Hence, possibilities are sought to speed-up this process and make it more time-efficient. In this study, two artificial neural networks (ANNs) are developed to determine an optimal design of the Counihan hardware, i.e., castellated barrier wall, vortex generators, and surface roughness, in order to simulate the ABL flow developing above urban, suburban, and rural terrains, as previous ANN models were created for one terrain type only. A standard procedure is used in developing those two ANNs in order to further enhance best-practice possibilities rather than to improve existing ANN designing methodology. In total, experimental results obtained using 23 different hardware setups are used when creating ANNs. In those tests, basic barrier height, barrier castellation height, spacing density, and height of surface roughness elements are the parameters that were varied to create satisfactory ABL simulations. The first ANN was used for the estimation of mean wind velocity, turbulent Reynolds stress, turbulence intensity, and length scales, while the second one was used for the estimation of the power spectral density of velocity fluctuations. This extensive set of studied flow and turbulence parameters is unmatched in comparison to the previous relevant studies, as it includes here turbulence intensity and power spectral density of velocity fluctuations in all three directions, as well as the Reynolds stress profiles and turbulence length scales. Modeling results agree well with experiments for all terrain types, particularly in the lower ABL within the height range of the most engineering structures, while exhibiting sensitivity to abrupt changes and data scattering in profiles of wind-tunnel results. The

  2. Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network

    PubMed Central

    Zhang, Yunong; Zhang, Xiang; Chen, Jinxia; Lv, Linsheng; Ma, Huijuan; Wu, Xiaoming; Zhao, Weihong; Lou, Tanqi

    2013-01-01

    Background Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance. Methods A total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation. Results In the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m2 vs. a range from 71.3 to 101.7 ml/min/1.73 m2, allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network. Conclusions A new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse

  3. [Artificial neural networks for the identification of infrared spectra of Ilex Kudingcha].

    PubMed

    Pang, Tao-Tao; Yao, Jian-Bin; Du, Li-Ming

    2007-07-01

    In order to identify Ilex Kudingcha, two kinds of models of artificial neural networks (ANN), i.e. competitive neural network and back propagation neural network, were used to analyze their infrared spectra. Ilex Kudingcha samples were collected by Fourier transform infrared (FTIR) spectra. Twenty five samples were gathered as a train set, and 11 samples as a test set, then their training was performed using two networks each. The results show that the identification of Ilex Kudingcha from different areas can be effectively performed with the competitive neural network and BP network, but the competitive neural network is used in the identification of different grades of Ilex Kudingcha. The results were better in training speed and accuracy with the competitive neural network. In conclusion, the competitive neural network combined with FTIR spectroscopy is a good method for the identification of Ilex Kudingcha.

  4. Artificial Neural Network Test Support Development for the Space Shuttle PRCS Thrusters

    NASA Technical Reports Server (NTRS)

    Lehr, Mark E.

    2005-01-01

    A significant anomaly, Fuel Valve Pilot Seal Extrusion, is affecting the Shuttle Primary Reaction Control System (PRCS) Thrusters, and has caused 79 to fail. To help address this problem, a Shuttle PRCS Thruster Process Evaluation Team (TPET) was formed. The White Sands Test Facility (WSTF) and Boeing members of the TPET have identified many discrete valve current trace characteristics that are predictive of the problem. However, these are difficult and time consuming to identify and trend by manual analysis. Based on this exhaustive analysis over months, 22 thrusters previously delivered by the Depot were identified as high risk for flight failures. Although these had only recently been installed, they had to be removed from Shuttles OV103 and OV104 for reprocessing, by directive of the Shuttle Project Office. The resulting impact of the thruster removal, replacement, and valve replacement was significant (months of work and hundreds of thousands of dollars). Much of this could have been saved had the proposed Neural Network (NN) tool described in this paper been in place. In addition to the significant benefits to the Shuttle indicated above, the development and implementation of this type of testing will be the genesis for potential Quality improvements across many areas of WSTF test data analysis and will be shared with other NASA centers. Future tests can be designed to incorporate engineering experience via Artificial Neural Nets (ANN) into depot level acceptance of hardware. Additionally, results were shared with a NASA Engineering and Safety Center (NESC) Super Problem Response Team (SPRT). There was extensive interest voiced among many different personnel from several centers. There are potential spin-offs of this effort that can be directly applied to other data acquisition systems as well as vehicle health management for current and future flight vehicles.

  5. An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

    DTIC Science & Technology

    1990-06-01

    training is based on the concept of enforced performance. A neural network will learn to meet a specific performance goal if the performance standard...is the only solution to a problem. Performance index training is devised to teach the neural network the time-optimal control law for the system. Real...time adaptation of a neural network in closed loop control of the Crew/Equipment Retriever was demonstrated in computer simulations.

  6. Applying artificial neural networks to predict communication risks in the emergency department.

    PubMed

    Bagnasco, Annamaria; Siri, Anna; Aleo, Giuseppe; Rocco, Gennaro; Sasso, Loredana

    2015-10-01

    To describe the utility of artificial neural networks in predicting communication risks. In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. A multicentre observational study. Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. © 2015 John Wiley & Sons Ltd.

  7. Resource constrained design of artificial neural networks using comparator neural network

    NASA Technical Reports Server (NTRS)

    Wah, Benjamin W.; Karnik, Tanay S.

    1992-01-01

    We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

  8. Resonant hopping of a robot controlled by an artificial neural oscillator.

    PubMed

    Pelc, Evan H; Daley, Monica A; Ferris, Daniel P

    2008-06-01

    The bouncing gaits of terrestrial animals (hopping, running, trotting) can be modeled as a hybrid dynamic system, with spring-mass dynamics during stance and ballistic motion during the aerial phase. We used a simple hopping robot controlled by an artificial neural oscillator to test the ability of the neural oscillator to adaptively drive this hybrid dynamic system. The robot had a single joint, actuated by an artificial pneumatic muscle in series with a tendon spring. We examined how the oscillator-robot system responded to variation in two neural control parameters: descending neural drive and neuromuscular gain. We also tested the ability of the oscillator-robot system to adapt to variations in mechanical properties by changing the series and parallel spring stiffnesses. Across a 100-fold variation in both supraspinal gain and muscle gain, hopping frequency changed by less than 10%. The neural oscillator consistently drove the system at the resonant half-period for the stance phase, and adapted to a new resonant half-period when the muscle series and parallel stiffnesses were altered. Passive cycling of elastic energy in the tendon accounted for 70-79% of the mechanical work done during each hop cycle. Our results demonstrate that hopping dynamics were largely determined by the intrinsic properties of the mechanical system, not the specific choice of neural oscillator parameters. The findings provide the first evidence that an artificial neural oscillator will drive a hybrid dynamic system at partial resonance.

  9. Artificial neural networks in evaluation and optimization of modified release solid dosage forms.

    PubMed

    Ibrić, Svetlana; Djuriš, Jelena; Parojčić, Jelena; Djurić, Zorica

    2012-10-18

    Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

  10. Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

    PubMed

    Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun

    2013-02-15

    This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.

  11. [Building artificial neural networks model on portable NIR integrity wheat component measuring apparatus].

    PubMed

    Ji, Hai-yan; Wen, Ming; Hao, Bin

    2006-01-01

    The quantitative analysis model of protein in integrity wheat was built by three layers back propagation artificial neural networks for portable near infrared (NIR) integrity wheat component measuring apparatus. The structure diagram of integrity wheat component measuring apparatus, light route structure of apparatus and the spectrum of integrity wheat were given in the present paper. The theory of artificial neural network was briefly introduced and the results of quantitative analysis model of protein were given. For calibration set and prediction set, the correlation coefficient was 0.90 and 0.96 respectively; the relative standard deviation is 3.77% and 4.46% respectively. Because of the influence of light route structure, electrical circuit, and integrity sample forms on the measuring apparatus, some nonlinearity exists between the spectral parameters and chemical values. The results of artificial neural networks nonlinear model were superior to linear model.

  12. Application of Artificial Neural Network to Predict the use of Runway at Juanda International Airport

    NASA Astrophysics Data System (ADS)

    Putra, J. C. P.; Safrilah

    2017-06-01

    Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.

  13. Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

    PubMed Central

    Ibrić, Svetlana; Djuriš, Jelena; Parojčić, Jelena; Djurić, Zorica

    2012-01-01

    Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms. PMID:24300369

  14. Beneficial role of noise in artificial neural networks

    SciTech Connect

    Monterola, Christopher; Saloma, Caesar; Zapotocky, Martin

    2008-06-18

    We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated.

  15. Application of artificial neural networks in nonlinear analysis of trusses

    NASA Technical Reports Server (NTRS)

    Alam, J.; Berke, L.

    1991-01-01

    A method is developed to incorporate neural network model based upon the Backpropagation algorithm for material response into nonlinear elastic truss analysis using the initial stiffness method. Different network configurations are developed to assess the accuracy of neural network modeling of nonlinear material response. In addition to this, a scheme based upon linear interpolation for material data, is also implemented for comparison purposes. It is found that neural network approach can yield very accurate results if used with care. For the type of problems under consideration, it offers a viable alternative to other material modeling methods.

  16. Detection, location, and quantification of structural damage by neural-net-processed moiré profilometry

    NASA Astrophysics Data System (ADS)

    Grossman, Barry G.; Gonzalez, Frank S.; Blatt, Joel H.; Hooker, Jeffery A.

    1992-03-01

    The development of efficient high speed techniques to recognize, locate, and quantify damage is vitally important for successful automated inspection systems such as ones used for the inspection of undersea pipelines. Two critical problems must be solved to achieve these goals: the reduction of nonuseful information present in the video image and automatic recognition and quantification of extent and location of damage. Artificial neural network processed moire profilometry appears to be a promising technique to accomplish this. Real time video moire techniques have been developed which clearly distinguish damaged and undamaged areas on structures, thus reducing the amount of extraneous information input into an inspection system. Artificial neural networks have demonstrated advantages for image processing, since they can learn the desired response to a given input and are inherently fast when implemented in hardware due to their parallel computing architecture. Video moire images of pipes with dents of different depths were used to train a neural network, with the desired output being the location and severity of the damage. The system was then successfully tested with a second series of moire images. The techniques employed and the results obtained are discussed.

  17. Can artificial neural networks provide an "expert's" view of medical students performances on computer based simulations?

    PubMed Central

    Stevens, R. H.; Najafi, K.

    1992-01-01

    Artificial neural networks were trained to recognize the test selection patterns of students' successful solutions to seven immunology computer based simulations. When new student's test selections were presented to the trained neural network, their problem solutions were correctly classified as successful or non-successful > 90% of the time. Examination of the neural networks output weights after each test selection revealed a progressive increase for the relevant problem suggesting that a successful solution was represented by the neural network as the accumulation of relevant tests. Unsuccessful problem solutions revealed two patterns of students performances. The first pattern was characterized by low neural network output weights for all seven problems reflecting extensive searching and lack of recognition of relevant information. In the second pattern, the output weights from the neural network were biased towards one of the remaining six incorrect problems suggesting that the student mis-represented the current problem as an instance of a previous problem. PMID:1482863

  18. Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: pilot study.

    PubMed

    Asada, N; Doi, K; MacMahon, H; Montner, S M; Giger, M L; Abe, C; Wu, Y

    1990-12-01

    An artificial neural network approach was applied to the differential diagnosis of interstitial lung diseases. The neural network was designed to distinguish between nine types of interstitial lung diseases on the basis of 20 items of clinical and radiographic information. A data base for training and testing the neural network was created with 10 hypothetical cases for each of the nine diseases. The performance of the neural network was evaluated by means of receiver operating characteristic analysis. The decision performance of the neural network was high; it was comparable to that of chest radiologists and superior to that of senior radiology residents. The preliminary results strongly suggest that the neural network approach has potential utility in the computer-aided differential diagnosis of interstitial lung diseases.

  19. The Application of Artificial Neural Networks to Astronomical Classification

    NASA Astrophysics Data System (ADS)

    Naim, A.

    1995-12-01

    Galaxies are fundamental to the understanding of the structure and evolution of the universe. They contain stars, gas and dust, and serve as an astrophysical laboratory in which physical processes can be examined. In the context of the large scale structure of the universe galaxies can be viewed as test particles. They are bright and therefore visible at very large distances, and also numerous and so can be used to provide reliable statistics. In previous decades the major obstacle to studying the large scale structure of the universe was the relatively sparse data samples, because obtaining large quantities of galaxian images and spectra requires a lot of observing time, and the accumulation of significant data bases was therefore a slow process. This obstacle is in the process of being removed today, with the advent of large-scale surveys (e.g., the APM galaxy survey, the Sloan Digital Sky Survey and the 2 degree Field survey). The new challenge with which the astronomical community is faced is the management and analysis of the forthcoming extragalactic data bases. On top of the obvious need for better hardware to give large storage volumes and quick access, one needs to devise automated tools for data analysis. The sheer volume of the data renders manual analysis impractical. It would be best if one could somehow transfer the knowledge and expertise accumulated over years of painstaking manual analysis to a machine. This thesis is part of an effort to achieve this goal. I borrowed techniques that have proved useful in other fields (e.g., engineering) and applied them to astronomical datasets. The major tool I used was Artificial Neural Networks (ANNs), which was originally conceived as a simplified computational model for the brain. The scope of methods and algorithms referred to as ANNs is quite wide. In particular, a distinction is made between Supervised Learning algorithms and Unsupervised methods. The former put the emphasis on ``teaching'' a machine to do

  20. Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

    NASA Astrophysics Data System (ADS)

    Lenhardt, L.; Zeković, I.; Dramićanin, T.; Dramićanin, M. D.

    2013-11-01

    Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

  1. Classification of Acousto-Optic Correlation Signatures of Spread Spectrum Signals Using Artificial Neural Networks

    DTIC Science & Technology

    1989-12-01

    Ohio ’aPw iorlipuab muo i 0I2, AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL...ENG/89D- 10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL NEURAL NETWORKS THESIS John W. DeBerry...Captain, USAF AFIT/GE/ENG/89D- 10 Approved for public release; distribution unlimited. AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION

  2. Artificial neural network to predict degradation of non-metallic lining materials from laboratory tests

    SciTech Connect

    Silverman, D.C.

    1994-12-31

    Artificial neural networks are computer simulations that have the potential of ``finding`` the same patterns that corrosion practitioners recognize to relate experimental test results to lifetime predictions. This potential ability was utilized to construct an artificial neural network to recognize the pattern between results from a sequential immersion test for organic non-metallic lining materials and their ability to function as linings in actual applications. The network so constructed has been shown to predict field performance from this test. The network was incorporated within an Expert System to simplify data input and output, allow for simple consistency checks, and to make the final prediction.

  3. Frequency-coded artificial neural networks: An approach to self-organizing systems

    SciTech Connect

    Dress, W.B.

    1987-01-01

    A frequency-based model of an artificial neural network is being explored for active learning in a simulated environment and for its response to multiple modalities of input data. Physical sensors couple naturally to such a network, providing an easy migration path from simulation to application. The combination of an artificial neural network processing frequency-coded sensor information and implemented on advanced computer architectures is seen as an answer to the problems arising in robotics and the fusion of large quantities of multisensory data.

  4. Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Pchelintseva, Svetlana V.; Runnova, Anastasia E.; Musatov, Vyacheslav Yu.; Hramov, Alexander E.

    2017-03-01

    In the paper we study the problem of recognition type of the observed object, depending on the generated pattern and the registered EEG data. EEG recorded at the time of displaying cube Necker characterizes appropriate state of brain activity. As an image we use bistable image Necker cube. Subject selects the type of cube and interpret it either as aleft cube or as the right cube. To solve the problem of recognition, we use artificial neural networks. In our paper to create a classifier we have considered a multilayer perceptron. We examine the structure of the artificial neural network and define cubes recognition accuracy.

  5. Modeling sulphur dioxide due to vehicular traffic using artificial neural network.

    PubMed

    Singh, B K; Singh, A K; Prasad, S C

    2009-10-01

    The dispersion characteristics of vehicular exhaust are highly non-linear. The deterministic as well as numerical models are unable to predict these air pollutants precisely. Artificial neural network (ANN), having the capability to recognize the non-linearity present in the noisy data, has been used in the present work to model the emission concentration of sulphur dioxide from vehicular source in an urban area. ANN model is developed with different combinations of traffic and meteorological parameters. The model prediction reveals that the artificial neural network trained with both traffic and meteorological parameters together shows better performance in predicting SO2 concentration.

  6. Simulation of concept acquisition according to Posner's theory using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Grzegorczyk, Dawid; Nieznański, Marek; Mulawka, Jan J.

    2011-10-01

    The prototype model of classification assumes that categories are stored in human mind as abstracted summary representations formed in the process of experiencing specimens. Classification of new exemplars is based on their similarity to the abstracted prototype. From studies using Michael Posner"s dot-pattern recognition paradigm, we selected several empirical observations, like category size effect, category breadth effect or prototype-exemplar similarity effect, and tested them on artificial neural networks. In this work we show that the properties of human categorization process can be very well simulated and observed on artificial neural networks.

  7. Improved diagnosis of breast implant rupture with sonographic findings and artificial neural networks.

    PubMed

    Venta, L A; Salchenberger, L M; Venta, E R

    1998-04-01

    The authors evaluated the use of sonographic findings combined with artificial neural networks as an aid to the diagnosis of breast implant rupture. From a database of 78 breast implants that were evaluated prospectively with sonography and then surgically removed, sonographic findings and surgical results were used to train and test backpropagation and radial basis function artificial neural networks by using the leave-one-out method. Receiver operating characteristic (ROC) curve analysis was used to compare the performance of the different neural networks with that of the radiologists involved. By using the ROC area index as a measure of performance, the artificial neural network (Az = 0.8744) outperformed the radiologists (Az = 0.8057), although not by a statistically significant difference (P = .09). The best-performing network used, in addition to the sonographic findings, the diagnosis of the radiologist as an input. This network (Az = 0.9245) outperformed both the radiologists and the "unaided" networks by a statistically significant margin (P = .02 for radiologists, P = .04 for the unaided network). The network performed remarkably well in those cases in which the radiologists classified the implant as indeterminate, predicting the correct diagnosis in 23 of 25 cases (92%). The results suggest that artificial neural networks in tandem with the unaided radiologic diagnosis can improve the accuracy rate in the detection of implant rupture based on sonographic findings. This "team" approach provided the best results.

  8. Risk stratification in heart failure using artificial neural networks.

    PubMed Central

    Atienza, F.; Martinez-Alzamora, N.; De Velasco, J. A.; Dreiseitl, S.; Ohno-Machado, L.

    2000-01-01

    Accurate risk stratification of heart failure patients is critical to improve management and outcomes. Heart failure is a complex multisystem disease in which several predictors are categorical. Neural network models have successfully been applied to several medical classification problems. Using a simple neural network, we assessed one-year prognosis in 132 patients, consecutively admitted with heart failure, by classifying them in 3 groups: death, readmission and one-year event-free survival. Given the small number of cases, the neural network model was trained using a resampling method. We identified relevant predictors using the Automatic Relevance Determination (ARD) method, and estimated their mean effect on the 3 different outcomes. Only 9 individuals were misclassified. Neural networks have the potential to be a useful tool for making prognosis in the domain of heart failure. PMID:11079839

  9. Classification of Respiratory Sounds by Using An Artificial Neural Network

    DTIC Science & Technology

    2007-11-02

    are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.

  10. 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.

  11. 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.

  12. Cascaded-grating holography for artificial neural networks

    NASA Astrophysics Data System (ADS)

    Owechko, Y.

    1993-03-01

    A holographic technique for implementing optical neural networks is described in which each connection weight is distributed among a series of cascaded angularly and spatially multiplexed photorefractive gratings. This greatly reduces cross talk caused by the conical Bragg degeneracy associated with a single grating, reduces distortions caused by beam coupling, and improves the utilization of pixels in the input spatial light modulator. Experimental examples of high-quality holographic storage and optical implementations of the perceptron and backpropagation neural networks are also discussed.

  13. HL-2A tokamak disruption forecasting based on an artificial neural network

    NASA Astrophysics Data System (ADS)

    Wang, Hao; Wang, Ai-Ke; Yang, Qing-Wei; Ding, Xuan-Tong; Dong, Jia-Qi; H, Sanuki; H, Itoh

    2007-12-01

    Artificial neural networks are trained to forecast the plasma disruption in HL-2A tokamak. Optimized network architecture is obtained. Saliency analysis is made to assess the relative importance of different diagnostic signals as network input. The trained networks can successfully detect the disruptive pulses of HL-2A tokamak. The results obtained show the possibility of developing a neural network predictor that intervenes well in advance for avoiding plasma disruption or mitigating its effects.

  14. Customizable VLSI artificial neural network chips based on a novel technology

    SciTech Connect

    Fu, C. Y.; Law, B.; Chapline, G.; Swenson, D.

    1993-09-14

    The human cerebral cortex contains approximately 10{sup 11} neurons and 10{sup 14} synapses. It thus seems logical that any technology intended to mimic human capabilities should have the ability to fabricate a very large number of neurons and even larger numbers of synapses. This paper describes an implementation of hardware neural networks using highly linear thin-film resistor technology and an 8-bit binary weight circuit to produce customizable artificial neural network chips and systems.

  15. Towards the fabrication of artificial 3D microdevices for neural cell networks.

    PubMed

    Gill, Andrew A; Ortega, Ílida; Kelly, Stephen; Claeyssens, Frederik

    2015-04-01

    This work reports first steps towards the development of artificial neural stem cell microenvironments for the control and assessment of neural stem cell behaviour. Stem cells have been shown to be found in specific, supportive microenvironments (niches) and are believed to play an important role in tissue regeneration mechanisms. These environments are intricate spaces with chemical and biological features. Here we present work towards the development of physically defined microdevices in which neural and neural stem cells can be studied in 3-dimensions. We have approached this challenge by creating bespoke, microstructured polymer environments using both 2-photon polymerisation and soft lithography techniques. Specifically, we have designed and fabricated biodegradable microwell-shaped devices using an in house synthetized polymer (4-arm photocurable poly-lactid acid) on a bespoke 2-photon polymerisation (2PP) set-up. We have studied swelling and degradation of the constructs as well as biocompatibility. Moreover, we have explored the potential of these constructs as artificial neural cell substrates by culturing NG108-15 cells (mouse neuroblastoma; rat glioma hybrid) and human neural progenitor cells on the microstructures. Finally, we have studied the effects of our artificial microenvironments upon neurite length and cell density.

  16. On Design and Implementation of Neural-Machine Interface for Artificial Legs

    PubMed Central

    Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan (Lindsay); Yang, Qing

    2011-01-01

    The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees’ intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user’s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user’s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs. PMID:22389637

  17. On Design and Implementation of Neural-Machine Interface for Artificial Legs.

    PubMed

    Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan Lindsay; Yang, Qing; Huang, He

    2011-09-06

    The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees' intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user's intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user's intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs.

  18. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    PubMed

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  19. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    USDA-ARS?s Scientific Manuscript database

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  20. Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

    PubMed

    Buscema, Paolo Massimo; Massini, Giulia; Maurelli, Guido

    2014-10-01

    The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

  1. An Examination of Application of Artificial Neural Network in Cognitive Radios

    NASA Astrophysics Data System (ADS)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  2. Analogue spin-orbit torque device for artificial-neural-network-based associative memory operation

    NASA Astrophysics Data System (ADS)

    Borders, William A.; Akima, Hisanao; Fukami, Shunsuke; Moriya, Satoshi; Kurihara, Shouta; Horio, Yoshihiko; Sato, Shigeo; Ohno, Hideo

    2017-01-01

    We demonstrate associative memory operations reminiscent of the brain using nonvolatile spintronics devices. Antiferromagnet-ferromagnet bilayer-based Hall devices, which show analogue-like spin-orbit torque switching under zero magnetic fields and behave as artificial synapses, are used. An artificial neural network is used to associate memorized patterns from their noisy versions. We develop a network consisting of a field-programmable gate array and 36 spin-orbit torque devices. An effect of learning on associative memory operations is successfully confirmed for several 3 × 3-block patterns. A discussion on the present approach for realizing spintronics-based artificial intelligence is given.

  3. Extraction and optimization of classification rules for continuous or mixed-mode data using neural nets

    NASA Astrophysics Data System (ADS)

    Wang, Dianhui; Dillon, T. S.

    2001-03-01

    Extracting and optimizing rules from continuous or mixed- mode data directly for pattern classification problems is a challenging problem. Self-organizing neural-nets are employed to initialize the rules. A regularization model which trades off misclassification rate, recognition rate and generalization ability is first presented for refining the initial rules. To generate rules for patterns with lower probability density but considerable conceptual importance, an approach to iteratively resolving the clustering part for a filtered set of data is used. The methodology is evaluated using Iris data and demonstrates the effectiveness of technique.

  4. Total solar irradiance reconstruction using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Tebabal Yirdaw, Ambelu; Damtie, Baylie; Nigussie, Melessew; Bires, Abiyot; Yizengaw, Endawoke

    2015-08-01

    A feed-forward neural network which can account for nonlinear relationships was used to reconstruct total solar irradiance (TSI). A single layer feed forward neural network with back-propagation algorithm have been implemented for reconstructing daily total solar irradiance from daily photometric sunspot index, and core to wing ratio of Mg II index data. The data year from 1978 to 2013 was used for the training, validation and testing purpose. In order to obtain the optimum neural network for TSI reconstruction, the root mean square error (RMSE), mean absolute error (MAE) and regression coefficient have been taken into account. We have carried out the analysis is made by comparing the reconstructed TSI from neural networks (NNs ) and TSI measurement from satellite. We have found out that the reconstructed TSI and the PMOD composite have the correlation coefficient of about R=0.9307 over the span of the recorded, 1978 to 2013. The NNs model output indicates that reconstructed TSI from solar proxies (photometric index and MgII ) can explain 86.6% of the variance of TSI. Neural network is able to recreate TSI observations on a time scale of a day. This reconstructed TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

  5. A Model for Improving the Learning Curves of Artificial Neural Networks.

    PubMed

    Monteiro, Roberto L S; Carneiro, Tereza Kelly G; Fontoura, José Roberto A; da Silva, Valéria L; Moret, Marcelo A; Pereira, Hernane Borges de Barros

    2016-01-01

    In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree) for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.

  6. Application of Artificial Neural Networks to the Design of Turbomachinery Airfoils

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan; Madavan, Nateri

    1997-01-01

    Artificial neural networks are widely used in engineering applications, such as control, pattern recognition, plant modeling and condition monitoring to name just a few. In this seminar we will explore the possibility of applying neural networks to aerodynamic design, in particular, the design of turbomachinery airfoils. The principle idea behind this effort is to represent the design space using a neural network (within some parameter limits), and then to employ an optimization procedure to search this space for a solution that exhibits optimal performance characteristics. Results obtained for design problems in two spatial dimensions will be presented.

  7. Application of Artificial Neural Networks to the Design of Turbomachinery Airfoils

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan; Madavan, Nateri

    1997-01-01

    Artificial neural networks are widely used in engineering applications, such as control, pattern recognition, plant modeling and condition monitoring to name just a few. In this seminar we will explore the possibility of applying neural networks to aerodynamic design, in particular, the design of turbomachinery airfoils. The principle idea behind this effort is to represent the design space using a neural network (within some parameter limits), and then to employ an optimization procedure to search this space for a solution that exhibits optimal performance characteristics. Results obtained for design problems in two spatial dimensions will be presented.

  8. Forecasting Plasma Disruptions Instabilities Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Andresia de Oliveira, Kenya; Vannucci, Alvaro; da Silva, Elton Cesar

    2000-10-01

    Two-layer feedforward neural network was used to forecast the disruptive instabilities observed in the TEXT tokamak plasma discharges. For this task, soft X-ray experimental signals were used with very promising results. It was verified that a neural network with an architecture of the type m:2m:m:1 (where m=15) was capable, for example, to forecast the disruptive instabilities up to 4 ms in advance. This period of time is about four times larger than the one obtained previously, when experimental magnetic signals from Mirnov coils were used. From these very good forecasting results obtained it can be concluded that neural networks might be considered an important tool for making predictions over the time evolution of nonlinear systems.

  9. Vein matching using artificial neural network in vein authentication systems

    NASA Astrophysics Data System (ADS)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  10. Analysis of pellet properties with use of artificial neural networks.

    PubMed

    Mendyk, Aleksander; Kleinebudde, Peter; Thommes, Markus; Yoo, Angelina; Szlęk, Jakub; Jachowicz, Renata

    2010-11-20

    The objective was to prepare neural models identifying relationships between formulation characteristics and pellet properties based on algorithmic approach of crucial variables selection and neuro-fuzzy systems application. The database consisted of information about 227 pellet formulations prepared by extrusion/spheronization method, with various model drugs and excipients. Cheminformatic description of excipients and model drugs was employed for numerical description of pellet formulations. Initial numbers of neural model inputs were up to around 3000. The inputs reduction procedure based on sensitivity analysis allowed to obtain less than 40 inputs for each model. The reduced models were subjects of fuzzy logic implementation resulting in logical rules tables providing human-readable rule sets applicable in future development of pellet formulations. Neural modeling enhanced knowledge about pelletization process and provided means for future computer-guided search for the optimal formulation.

  11. Performance of an artificial neural network for vertical root fracture detection: an ex vivo study.

    PubMed

    Kositbowornchai, Suwadee; Plermkamon, Supattra; Tangkosol, Tawan

    2013-04-01

    To develop an artificial neural network for vertical root fracture detection. A probabilistic neural network design was used to clarify whether a tooth root was sound or had a vertical root fracture. Two hundred images (50 sound and 150 vertical root fractures) derived from digital radiography--used to train and test the artificial neural network--were divided into three groups according to the number of training and test data sets: 80/120,105/95 and 130/70, respectively. Either training or tested data were evaluated using grey-scale data per line passing through the root. These data were normalized to reduce the grey-scale variance and fed as input data of the neural network. The variance of function in recognition data was calculated between 0 and 1 to select the best performance of neural network. The performance of the neural network was evaluated using a diagnostic test. After testing data under several variances of function, we found the highest sensitivity (98%), specificity (90.5%) and accuracy (95.7%) occurred in Group three, for which the variance of function in recognition data was between 0.025 and 0.005. The neural network designed in this study has sufficient sensitivity, specificity and accuracy to be a model for vertical root fracture detection. © 2012 John Wiley & Sons A/S.

  12. Intraportal Infusion of Ghrelin Could Inhibit Glucose-Stimulated GLP-1 Secretion by Enteric Neural Net in Wistar Rat

    PubMed Central

    Zhang, Xiyao; Li, Wensong; Li, Ping; Chang, Manli; Huang, Xu; Li, Qiang; Cui, Can

    2014-01-01

    As a regulator of food intake and energy metabolism, the role of ghrelin in glucose metabolism is still not fully understood. In this study, we determined the in vivo effect of ghrelin on incretin effect. We demonstrated that ghrelin inhibited the glucose-stimulated release of glucagon-like peptide-1 (GLP-1) when infused into the portal vein of Wistar rat. Hepatic vagotomy diminished the inhibitory effect of ghrelin on glucose-stimulated GLP-1 secretion. In addition, phentolamine, a nonselective α receptor antagonist, could recover the decrease of GLP-1 release induced by ghrelin infusion. Pralmorelin (an artificial growth hormone release peptide) infusion into the portal vein could also inhibit the glucose-stimulated release of GLP-1. And growth hormone secretagogue receptor antagonist, [D-lys3]-GHRP-6, infusion showed comparable increases of glucose stimulated GLP-1 release compared to ghrelin infusion into the portal vein. The data showed that intraportal infusion of ghrelin exerted an inhibitory effect on GLP-1 secretion through growth hormone secretagogue receptor 1α (GHS1α receptor), which indicated that the downregulation of ghrelin secretion after food intake was necessary for incretin effect. Furthermore, our results suggested that the enteric neural net involved hepatic vagal nerve and sympathetic nerve mediated inhibition effect of ghrelin on incretin effect. PMID:25247193

  13. Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

    NASA Astrophysics Data System (ADS)

    Güreşen, Erkam; Kayakutlu, Gülgün

    Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).

  14. A hardware implementation of artificial neural networks using field programmable gate arrays

    NASA Astrophysics Data System (ADS)

    Won, E.

    2007-11-01

    An artificial neural network algorithm is implemented using a low-cost field programmable gate array hardware. One hidden layer is used in the feed-forward neural network structure in order to discriminate one class of patterns from the other class in real time. In this work, the training of the network is performed in the off-line computing environment and the results of the training are configured to the hardware in order to minimize the latency of the neural computation. With five 8-bit input patterns, six hidden nodes, and one 8-bit output, the implemented hardware neural network makes decisions on a set of input patterns in 11 clock cycles, or less than 200 ns with a 60 MHz clock. The result from the hardware neural computation is well predictable based on the off-line computation. This implementation may be used in level 1 hardware triggers in high energy physics experiments.

  15. Fractional Fokker-Planck Equations and Artificial Neural Networks for Stochastic Control of Tokamak

    NASA Astrophysics Data System (ADS)

    Rastovic, Danilo

    2008-09-01

    The general form of description of Kolmogorov-Arnold-Moser (KAM) theorem in controlled plasma fusion, is obtained via the theory of artificial fuzzy neural networks. Without of the global maximum entropy principle, the complexity function is used for the Monte Carlo simulations.

  16. Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

    ERIC Educational Resources Information Center

    Perkins, Kyle; And Others

    1995-01-01

    This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

  17. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  18. The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

    ERIC Educational Resources Information Center

    Metz, Dale Evan; And Others

    1992-01-01

    A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…

  19. Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

    ERIC Educational Resources Information Center

    Anderson, Joan L.

    2006-01-01

    Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

  20. Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

    ERIC Educational Resources Information Center

    Perkins, Kyle; And Others

    1995-01-01

    This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

  1. The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

    ERIC Educational Resources Information Center

    Metz, Dale Evan; And Others

    1992-01-01

    A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…

  2. Reconstructing missing daily precipitation data using regression trees and artificial neural networks

    USDA-ARS?s Scientific Manuscript database

    Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

  3. Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas

    USDA-ARS?s Scientific Manuscript database

    The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANN) trained with a Backpropagation (BP) algor...

  4. A study of creep-fatigue life prediction using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Kwon, Young Il; Lim, Byeong Soo

    2001-07-01

    In this study, using AISI 316 stainless steel, creep-fatigue tests were carried out under various test conditions (different total strain ranges and hold times) to verify the applicability of the artificial neural network method to creep-fatigue life prediction. Life prediction was also made by the modified Coffin-Manson method and the modified Ostegren method using 21 data points out of a total 27 experimental data points. The six verification data points were carefully chosen for the purpose of evaluating the predictability of each method. The predicted lives were compared with the experimental results and the following conclusions were obtained within the scope of this study. While the creep-fatigue life prediction by the modified Coffin-Manson method and the modified Ostegren method had average errors of 35.8% and 47.7% respectively, the artificial neural network method had only 15.6%. As a result, the artificial neural network method with the adaptive learning rate was found to be far more accurate and effective than any of the others. The validity of the artificial neural network method for life prediction checked with the six verification data points also proved to be very satisfactory.

  5. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    ERIC Educational Resources Information Center

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  6. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    ERIC Educational Resources Information Center

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  7. Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

    ERIC Educational Resources Information Center

    Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

    2001-01-01

    Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

  8. Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

    ERIC Educational Resources Information Center

    Perkins, Kyle; And Others

    This paper reports the results of using a three-layer backpropagation artificial neural network to predict item difficulty in a reading comprehension test. Two network structures were developed, one with and one without a sigmoid function in the output processing unit. The data set, which consisted of a table of coded test items and corresponding…

  9. Application of an artificial neural network and morphing techniques in the redesign of dysplastic trochlea.

    PubMed

    Cho, Kyung Jin; Müller, Jacobus H; Erasmus, Pieter J; DeJour, David; Scheffer, Cornie

    2014-01-01

    Segmentation and computer assisted design tools have the potential to test the validity of simulated surgical procedures, e.g., trochleoplasty. A repeatable measurement method for three dimensional femur models that enables quantification of knee parameters of the distal femur is presented. Fifteen healthy knees are analysed using the method to provide a training set for an artificial neural network. The aim is to use this artificial neural network for the prediction of parameter values that describe the shape of a normal trochlear groove geometry. This is achieved by feeding the artificial neural network with the unaffected parameters of a dysplastic knee. Four dysplastic knees (Type A through D) are virtually redesigned by way of morphing the groove geometries based on the suggested shape from the artificial neural network. Each of the four resulting shapes is analysed and compared to its initial dysplastic shape in terms of three anteroposterior dimensions: lateral, central and medial. For the four knees the trochlear depth is increased, the ventral trochlear prominence reduced and the sulcus angle corrected to within published normal ranges. The results show a lateral facet elevation inadequate, with a sulcus deepening or a depression trochleoplasty more beneficial to correct trochlear dysplasia.

  10. Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

    ERIC Educational Resources Information Center

    Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

    2001-01-01

    Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

  11. 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.

  12. Vibration monitoring of EDF rotating machinery using artificial neural networks

    SciTech Connect

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E. . Dept. of Nuclear Engineering); Hamon, L.; Lefevre, F. . Direction des Etudes et Recherches)

    1991-01-01

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.

  13. Vibration monitoring of EDF rotating machinery using artificial neural networks

    SciTech Connect

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E.; Hamon, L.; Lefevre, F.

    1991-12-31

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.

  14. A Search for top quark using artificial neural networks

    SciTech Connect

    Amidi, Erfan

    1996-02-01

    The neural networks method has been applied to 75 pb-1 of data collected by the D0 detector at Fermilab during the 1993-1995 p$\\bar{p}$ collider run at √s = 1.8 TeV, to isolate the top quark in the e+jets+ET channel.

  15. Modelling the spectral energy distribution of galaxies: introducing the artificial neural network

    NASA Astrophysics Data System (ADS)

    Silva, L.; Schurer, A.; Granato, G. L.; Almeida, C.; Baugh, C. M.; Frenk, C. S.; Lacey, C. G.; Paoletti, L.; Petrella, A.; Selvestrel, D.

    2011-01-01

    The spectral energy distribution (SED) of galaxies is a complex function of the star formation history and geometrical arrangement of stars and gas in galaxies. The computation of the radiative transfer of stellar radiation through the dust distribution is time-consuming. This aspect becomes unacceptable in particular when dealing with the predictions by semi-analytical galaxy formation models populating cosmological volumes, to be then compared with multi-wavelength surveys. Mainly for this aim, we have implemented an artificial neural network (ANN) algorithm into the spectro-photometric and radiative transfer code GRASIL in order to compute the SED of galaxies in a short computing time. This allows to avoid the adoption of empirical templates that may have nothing to do with the mock galaxies output by models. The ANN has been implemented to compute the dust emission spectrum (the bottleneck of the computation), and separately for the star-forming molecular clouds (MC) and the diffuse dust (due to their different properties and dependencies). We have defined the input neurons effectively determining their emission, which means this implementation has a general applicability and is not linked to a particular galaxy formation model. We have trained the net for the disc and spherical geometries, and tested its performance to reproduce the SED of disc and starburst galaxies, as well as for a semi-analytical model for spheroidal galaxies. We have checked that for this model both the SEDs and the galaxy counts in the Herschel bands obtained with the ANN approximation are almost superimposed to the same quantities obtained with the full GRASIL. We conclude that this method appears robust and advantageous, and will present the application to a more complex SAM in another paper.

  16. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    NASA Astrophysics Data System (ADS)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  17. Applications of Artificial Neural Networks in integrated water management: fiction or future?

    PubMed

    Schulze, F H; Wolf, H; Jansen, H W; van der Veer, P

    2005-01-01

    An Artificial Neural Network (ANN) is nowadays recognized as a very promising tool for relating input data to output data. It is said that the possibilities of artificial neural networks are unlimited. Here we focus on the potential role of neural networks in integrated water management. An Artificial Neural Network (ANN) is a mathematical methodology which describes relations between cause (input data) and effects (output data) irrespective of the process laying behind and without the need for making assumptions considering the nature of the relations. The applications are widespread and vary from optimization of measuring networks, operational water management, prediction of drinking water consumption, on-line steering of wastewater treatment plants and sewage systems, up to more specific applications such as establishing a relationship between the observed erosion of groyne field sediments and the characteristics of passing vessels on the river Rhine. Especially where processes are complex, neural networks can open new possibilities for understanding and modelling these kinds of complex processes. Besides explaining the method of ANN this paper shows different applications. Three examples have been worked out in more detail. An intelligent monitoring system is shown for the on-line prediction of water consumption, ANN are successfully used for sludge cost monitoring and optimizing wastewater treatment and the usage of ANN is shown in optimizing and monitoring water quality measuring networks. An ANN appears to be a multiuse and powerful tool for modelling complex processes.

  18. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    NASA Technical Reports Server (NTRS)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  19. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    NASA Technical Reports Server (NTRS)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  20. Artificial neural network based soft estimator for estimation of transducer static nonlinearity.

    PubMed

    Singh, Amar Partap; Kamal, Tara Singh; Kumar, Shakti

    2004-08-01

    In this work, the development of an Artificial Neural Network (ANN) based soft estimator is reported for the estimation of static-nonlinearity associated with the transducers. Under the realm of ANN based transducer modeling, only two neural models have been suggested to estimate the static-nonlinearity associated with the transducers with quite successful results. The first existing model is based on the concept of a functional link artificial neural network (FLANN) trained with mu-LMS (Least Mean Squares) learning algorithm. The second one is based on the architecture of a single layer linear ANN trained with alpha-LMS learning algorithm. However, both these models suffer from the problem of slow convergence (learning). In order to circumvent this problem, it is proposed to synthesize the direct model of transducers using the concept of a Polynomial-ANN (polynomial artificial neural network) trained with Levenberg-Marquardt (LM) learning algorithm. The proposed Polynomial-ANN oriented transducer model is implemented based on the topology of a single-layer feed-forward back-propagation-ANN. The proposed neural modeling technique provided an extremely fast convergence speed with increased accuracy for the estimation of transducer static nonlinearity. The results of convergence are very stimulating with the LM learning algorithm.