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Sample records for neural network discrimination

  1. Discriminating lysosomal membrane protein types using dynamic neural network.

    PubMed

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

    This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

  2. Event Discrimination using Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Menon, Hareesh; Hughes, Richard; Daling, Alec; Winer, Brian

    2017-01-01

    Convolutional Neural Networks (CNNs) are computational models that have been shown to be effective at classifying different types of images. We present a method to use CNNs to distinguish events involving the production of a top quark pair and a Higgs boson from events involving the production of a top quark pair and several quark and gluon jets. To do this, we generate and simulate data using MADGRAPH and DELPHES for a general purpose LHC detector at 13 TeV. We produce images using a particle flow algorithm by binning the particles geometrically based on their position in the detector and weighting the bins by the energy of each particle within each bin, and by defining channels based on particle types (charged track, neutral hadronic, neutral EM, lepton, heavy flavor). Our classification results are competitive with standard machine learning techniques. We have also looked into the classification of the substructure of the events, in a process known as scene labeling. In this context, we look for the presence of boosted objects (such as top quarks) with substructure encompassed within single jets. Preliminary results on substructure classification will be presented.

  3. Comparison of Sonar Discrimination by an Echolocating Dolphin and a Counterpropagation Neural Network

    DTIC Science & Technology

    1992-09-01

    AND SUBTITLE 5 FUNDING NUMBERS COMPARISON OF SONAR DISCRIMINATION BY AN ECHOLOCATING PR: MMB2 DOLPHIN AND A COUNTERPROPAGATION NEURAL NETWORK PE...COMPARISON OF SONAR DISCRIMINATION BY AN constanB-1 filter cal fe expressed as ECIHOLOCATING DOLPHIN AND A COUNTERPROPAGATION 2Q-1 NEURAL NETWORK ’ " -- 1...In this study, a counterpropagation artificial layer of N+1 elements, a Kohonen layer of N elements neural network was used to examine the broadband

  4. Application of Neural Networks to Seismic Signal Discrimination Research Findings

    DTIC Science & Technology

    1994-04-11

    waveforms, and the training and testing of neural networks for seismic event classification. It was necessary to utilize seismic events that had a high...degree of reliability for accurate training of the neural networks . The seismic waveforms were obtained from the Center for Seismic Studies and were

  5. Neural networks using broadband spectral discriminators reduces illumination required for broccoli identification in weedy fields

    NASA Astrophysics Data System (ADS)

    Hahn, Federico

    1996-03-01

    Statistical discriminative analysis and neural networks were used to prove that crop/weed/soil discrimination by optical reflectance was feasible. The wavelengths selected as inputs on those neural networks were ten nanometers width, reducing the total collected radiation for the sensor. Spectral data collected from several farms having different weed populations were introduced to discriminant analysis. The best discriminant wavelengths were used to build a wavelength histogram which selected the three best spectral broadbands for broccoli/weed/soil discrimination. The broadbands were analyzed using a new single broadband discriminator index named the discriminative integration index, DII, and the DII values obtained were used to train a neural network. This paper introduces the index concept, its results and its use for minimizing artificial lightning requirements with broadband spectral measurements for broccoli/weed/soil discrimination.

  6. Neural Network Aided Glitch-Burst Discrimination and Glitch Classification

    NASA Astrophysics Data System (ADS)

    Rampone, Salvatore; Pierro, Vincenzo; Troiano, Luigi; Pinto, Innocenzo M.

    2013-11-01

    We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored. In order to provide a proof of concept, we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-to-noise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset, and used to classify the transients as glitch or burst. A Self-Organizing Map (SOM) architecture is finally used to classify the glitches. The glitch/burst discrimination and glitch classification abilities are gauged in terms of the related truth tables. Preliminary results suggest that the approach is effective and robust throughout the SNR range of practical interest. Perspective applications pertain both to distributed (network, multisensor) detection of GWBs, where some intelligence at the single node level can be introduced, and instrument diagnostics/optimization, where spurious transients can be identified, classified and hopefully traced back to their entry points.

  7. Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Cai-Xun; Lin, Shin-Ted; Zhao, Jian-Ling; Yu, Xun-Zhen; Wang, Li; Zhu, Jing-Jun; Xing, Hao-Yang

    2016-08-01

    In this work, a new neutron and γ (n/γ) discrimination method based on an Elman Neural Network (ENN) is proposed to improve the discrimination performance of liquid scintillator (LS) detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network (BPNN) as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit (FOM) in n/γ discrimination. The FOM increases from 0.907 ± 0.034 to 0.953 ± 0.037 by using the new method of the ENN. The proposed n/γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection. Supported by National Natural Science Foundation of China (11275134,11475117)

  8. Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity

    PubMed Central

    Srinivasa, Narayan; Cho, Youngkwan

    2014-01-01

    A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns—both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity. PMID:25566045

  9. Broccoli/weed/soil discrimination by optical reflectance using neural networks

    NASA Astrophysics Data System (ADS)

    Hahn, Federico

    1995-04-01

    Broccoli is grown extensively in Scotland, and has become one of the main vegetables cropped, due to its high yields and profits. Broccoli, weed and soil samples from 6 different farms were collected and their spectra obtained and analyzed using discriminant analysis. High crop/weed/soil discrimination success rates were encountered in each farm, but the selected wavelengths varied in each farm due to differences in broccoli variety, weed species incidence and soil type. In order to use only three wavelengths, neural networks were introduced and high crop/weed/soil discrimination accuracies for each farm were achieved.

  10. Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

    SciTech Connect

    Hooman, A.; Mohammadzadeh, M

    2008-01-30

    Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curve (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions.

  11. Spatiotemporal discrimination in neural networks with short-term synaptic plasticity

    NASA Astrophysics Data System (ADS)

    Shlaer, Benjamin; Miller, Paul

    2015-03-01

    Cells in recurrently connected neural networks exhibit bistability, which allows for stimulus information to persist in a circuit even after stimulus offset, i.e. short-term memory. However, such a system does not have enough hysteresis to encode temporal information about the stimuli. The biophysically described phenomenon of synaptic depression decreases synaptic transmission strengths due to increased presynaptic activity. This short-term reduction in synaptic strengths can destabilize attractor states in excitatory recurrent neural networks, causing the network to move along stimulus dependent dynamical trajectories. Such a network can successfully separate amplitudes and durations of stimuli from the number of successive stimuli. Stimulus number, duration and intensity encoding in randomly connected attractor networks with synaptic depression. Front. Comput. Neurosci. 7:59., and so provides a strong candidate network for the encoding of spatiotemporal information. Here we explicitly demonstrate the capability of a recurrent neural network with short-term synaptic depression to discriminate between the temporal sequences in which spatial stimuli are presented.

  12. Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments.

    PubMed

    Fang, Shih-Hau; Lin, Tsung-Nan

    2008-11-01

    This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted kappa-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.

  13. Discrimination of Venezuelan spirituous beverages by a trace element-radial basis neural network approach.

    PubMed

    Hernández-Caraballo, Edwin A; Avila de Hernández, Rita M; Rivas-Echeverría, Francklin; Capote-Luna, Tarcisio

    2008-01-15

    Radial basis neural networks (RBNNs) were developed and evaluated for discrimination of specimens of 'aguardiente de Cocuy', a spirituous beverage produced in the northwestern region of Venezuela. The beverage is distilled from the must of Agave cocui Trelease in an artisanship fashion with little quality control. Forty specimens, with known concentrations of copper, iron, and zinc, were used in this study. The specimens were previously collected in various locations around Sucre Municipality (Falcón State) and Urdaneta Municipality (Lara State). The normalized concentrations of these elements served as indirect descriptors of origin (input data). They were presented to the neural networks through 1-3 input nodes in seven different combinations. In addition, two categories (two collection sites) and four categories (two collection sites+two manufacturing conditions) were designated as output data, in order to assess the impact of such selection on the discrimination performance. The overall performance of the four-category RBNNs was as follows (the input data is indicated in parentheses): (Cu-Fe)>(Cu-Zn)>(Cu)>(Zn)>(Fe-Zn)>(Cu-Fe-Zn)>(Fe). In this case, the highest percentage of correct hits was 82.5%. For the two-category RBNNs, the performance decreased as indicated below: (Cu)>(Cu-Fe)>(Cu-Zn)>(Fe-Zn)>(Zn) approximately (Cu-Fe-Zn)>(Fe). The reduction in the number of categories led to an increase in the discrimination performance of all the RBNNs, the best of which was 90.0%. The possibility of discriminating specimens of 'aguardiente de Cocuy' with such an accuracy, based on a single-element determination, is particularly attractive as it would result in a reduction of analysis' costs and laboratory's response time.

  14. Recognition of Protozoa and Metazoa using image analysis tools, discriminant analysis, neural networks and decision trees.

    PubMed

    Ginoris, Y P; Amaral, A L; Nicolau, A; Coelho, M A Z; Ferreira, E C

    2007-07-09

    Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. This work presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes, with high values for the overall species recognition with the exception of sessile protozoa. In terms of the wastewater conditions assessment the obtained results were found to be suitable for the prediction of these conditions. Finally, the discriminant analysis and neural networks results were found to be quite similar whereas the decision trees technique was less appropriate.

  15. A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

    PubMed

    Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

    2015-12-01

    This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

  16. Preliminary results of investigations into the use of artificial neural networks for discriminating gas chromatograph mass spectra of remote samples

    NASA Technical Reports Server (NTRS)

    Geller, Harold A.; Norris, Eugene; Warnock, Archibald, III

    1991-01-01

    Neural networks trained using mass spectra data from the National Institute of Standards and Technology (NIST) are studied. The investigations also included sample data from the gas chromatograph mass spectrometer (GCMS) instrument aboard the Viking Lander, obtained from the National Space Science Data Center. The work performed to data and the preliminary results from the training and testing of neural networks are described. These preliminary results are presented for the purpose of determining the viability of applying artificial neural networks in discriminating mass spectra samples from remote instrumentation such as the Mars Rover Sample Return Mission and the Cassini Probe.

  17. Comparing discriminant analysis and neural network for the determination of sex using femur head measurements.

    PubMed

    Alunni, Véronique; Jardin, Philippe du; Nogueira, Luisa; Buchet, Luc; Quatrehomme, Gérald

    2015-08-01

    The measurement of the femoral head is usually considered an interesting variable for the sex determination of skeletal remains. To date, there are few published reference measurements of the femoral head in a modern European population for the purpose of sex determination. In this study, 116 femurs from 58 individuals of the South of France (Nice Bone Collection, Nice, France) were studied. Three measurements of the femoral head were taken: the vertical head diameter (VHD), the transversal head diameter (THD) and the head circumference (HC). The results show that: (i) there is no statistical difference between the right and left femurs for each of the three measurements (VHD, THD and HC). Therefore we arbitrarily chose to use the measures from the right femurs (N=58) to pursue our experiments; (ii) the measurements of the femoral head are similar to those of contemporary American populations; (iii) the dimensions of the femoral head place the measurements of the French population somewhere between Germany or Croatia, and Spain; (iv) there is no significant secular trend (in contrast with the femoral neck diameter); (v) the femoral head measurement as a single variable is useful for sex determination: a 96.5% rate of accuracy was obtained using THD and HC measurements with the artificial neural network; and a 94.8% rate of accuracy using VHD, both with the discriminant analysis and the neural network.

  18. Radial basis function neural networks for nonlinear Fisher discrimination and Neyman-Pearson classification.

    PubMed

    Casasent, David; Chen, Xue-wen

    2003-01-01

    We propose a novel technique for the design of radial basis function (RBF) neural networks (NNs). To select various RBF parameters, the class membership information of training samples is utilized to produce new cluster classes. This allows emphasis of classification performance for certain class data rather than best overall classification. This allows us to control performance as desired and to approximate Neyman-Pearson classification. We also show that by properly choosing the desired output neuron levels, then the RBF hidden to output layer performs Fisher discrimination analysis, and that the full system performs a nonlinear Fisher analysis. Data on an agricultural product inspection problem and on synthetic data confirm the effectiveness of these methods.

  19. Rapid identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks.

    PubMed

    Manzoor, S; Moncayo, S; Navarro-Villoslada, F; Ayala, J A; Izquierdo-Hornillos, R; de Villena, F J Manuel; Caceres, J O

    2014-04-01

    Identification and discrimination of bacterial strains of same species exhibiting resistance to antibiotics using laser induced breakdown spectroscopy (LIBS) and neural networks (NN) algorithm is reported. The method has been applied to identify 40 bacterial strains causing hospital acquired infections (HAI), i.e. Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Salmonella typhimurium, Salmonella pullurum and Salmonella salamae. The strains analyzed included both isolated from clinical samples and constructed in laboratory that differ in mutations as a result of their resistance to one or more antibiotics. Small changes in the atomic composition of the bacterial strains, as a result of their mutations and genetic variations, were detected by the LIBS-NN methodology and led to their identification and classification. This is of utmost importance because solely identification of bacterial species is not sufficient for disease diagnosis and identification of the actual strain is also required. The proposed method was successfully able to discriminate strains of the same bacterial species. The optimized NN models provided reliable bacterial strain identification with an index of spectral correlation higher than 95% for the samples analyzed, showing the potential and effectiveness of the method to address the safety and social-cost HAI-related issue.

  20. Industrial defect discrimination applying infrared imaging spectroscopy and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Garcia-Allende, Pilar Beatriz; Conde, Olga M.; Madruga, Francisco J.; Cubillas, Ana M.; Lopez-Higuera, Jose M.

    2008-03-01

    A non-intrusive infrared sensor for the detection of spurious elements in an industrial raw material chain has been developed. The system is an extension to the whole near infrared range of the spectrum of a previously designed system based on the Vis-NIR range (400 - 1000 nm). It incorporates a hyperspectral imaging spectrograph able to register simultaneously the NIR reflected spectrum of the material under study along all the points of an image line. The working material has been different tobacco leaf blends mixed with typical spurious elements of this field such as plastics, cardboards, etc. Spurious elements are discriminated automatically by an artificial neural network able to perform the classification with a high degree of accuracy. Due to the high amount of information involved in the process, Principal Component Analysis is first applied to perform data redundancy removal. By means of the extension to the whole NIR range of the spectrum, from 1000 to 2400 nm, the characterization of the material under test is highly improved. The developed technique could be applied to the classification and discrimination of other materials, and, as a consequence of its non-contact operation it is particularly suitable for food quality control.

  1. Principles of odor coding and a neural network for odor discrimination.

    PubMed Central

    Schild, D

    1988-01-01

    A concept of olfactory coding is proposed. It describes the stimulus responses of all receptor cells by the use of vector spaces. The morphological convergence pattern between receptor cells and glomeruli is given in the same vector space as the receptor cell activities. The overall input of a glomerulus follows as the scalar product of the receptor cell activity vector and the vector of the glomerulus' convergence pattern. The proposed coding concept shows how the network of the olfactory bulb succeeds in discriminating odors with high selectivity. It is concluded that sets of mitral cells coding similar odors work very much in the way of mutually inhibited matched filters. This solves one main problem both in olfaction as well as real-time odor detection by an artificial nose, i.e., how the fairly low degree of selectivity of receptor cells or sensors is overcome by the neural network following the receptor stage. The formal description of olfactory coding suggests that quality perception which is invariant under concentration shifts is accomplished by an associative memory in the olfactory bulb. PMID:3233263

  2. Discrimination Analysis of Earthquakes and Man-Made Events Using ARMA Coefficients Determination by Artificial Neural Networks

    SciTech Connect

    AllamehZadeh, Mostafa

    2011-12-15

    A Quadratic Neural Networks (QNNs) model has been developed for identifying seismic source classification problem at regional distances using ARMA coefficients determination by Artificial Neural Networks (ANNs). We have devised a supervised neural system to discriminate between earthquakes and chemical explosions with filter coefficients obtained by windowed P-wave phase spectra (15 s). First, we preprocess the recording's signals to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Second, we use a QNNs system to obtain ARMA coefficients for feature extraction in the discrimination problem. The derived coefficients are then applied to the neural system to train and classification. In this study, we explore the possibility of using single station three-component (3C) covariance matrix traces from a priori-known explosion sites (learning) for automatically recognizing subsequent explosions from the same site. The results have shown that this feature extraction gives the best classifier for seismic signals and performs significantly better than other classification methods. The events have been tested, which include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and 61 earthquakes (mb = 5.0-6.5) recorded by the Iranian National Seismic Network (INSN). The 100% correct decisions were obtained between site explosions and some of non-site events. The above approach to event discrimination is very flexible as we can combine several 3C stations.

  3. An investigation of the digital discrimination of neutrons and γ rays with organic scintillation detectors using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Liu, G.; Aspinall, M. D.; Ma, X.; Joyce, M. J.

    2009-08-01

    The discrimination of neutron and γ-ray events in an organic scintillator has been investigated by using a method based on an artificial neural network (ANN). Voltage pulses arising from an EJ-301 organic liquid scintillation detector in a mixed radiation field have been recorded with a fast digital sampling oscilloscope. Piled-up events have been disentangled using a pile-up management unit based on a fitting method. Each individual pulse has subsequently been sent to a discrimination unit which discriminates neutron and γ-ray events with a method based on an artificial neural network. This discrimination technique has been verified by the corresponding mixed-field data assessed by time of flight (TOF). It is shown that the characterization of the neutrons and photons achieved by the discrimination method based on the ANN is consistent with that afforded by TOF. This approach enables events that are often as a result of scattering or pile-up to be identified and returned to the data set and affords digital discrimination of mixed radiation fields in a broad range of environments on the basis of training obtained with a single TOF dataset.

  4. Analysis of Sileye-3/Alteino data with a neural network technique: Particle discrimination and energy reconstruction

    NASA Astrophysics Data System (ADS)

    Scrimaglio, R.; Rantucci, E.; Segreto, E.; Nurzia, G.; Finetti, N.; Di Gaetano, A.; Tassoni, A.; Picozza, P.; Narici, L.; Casolino, M.; Di Fino, L.; Rinaldi, A.; Zaconte, V.

    In this work, we present the data analysis of the Sileye-3/Alteino experiment with neural network technique. Sileye-3/Alteino is composed of two devices: the cosmic ray-advanced silicon telescope (an 8 plane, 32 strip silicon detector) and an electroencephalograph. It was placed on board the ISS on April the 27th 2002 to investigate on the Light Flash phenomenon and the radiation environment in space. We show the possibility of using neural networks as an useful tool for real-time data analysis. A feed-forward neural network (Multi-Layer Perceptron MLP) has been implemented and trained (with Monte Carlo data) to perform on line particle identification for ions with Atomic Number (Z) ⩽8 and incident kinetic energy reconstruction for ions Z > 2. The result of the analysis of Sileye-3/Alteino real data with the neural network and the improvements over classical analysis techniques are discussed.

  5. Neural-activity mapping of memory-based dominance in the crow: neural networks integrating individual discrimination and social behaviour control.

    PubMed

    Nishizawa, K; Izawa, E-I; Watanabe, S

    2011-12-01

    Large-billed crows (Corvus macrorhynchos), highly social birds, form stable dominance relationships based on the memory of win/loss outcomes of first encounters and on individual discrimination. This socio-cognitive behaviour predicts the existence of neural mechanisms for integration of social behaviour control and individual discrimination. This study aimed to elucidate the neural substrates of memory-based dominance in crows. First, the formation of dominance relationships was confirmed between males in a dyadic encounter paradigm. Next, we examined whether neural activities in 22 focal nuclei of pallium and subpallium were correlated with social behaviour and stimulus familiarity after exposure to dominant/subordinate familiar individuals and unfamiliar conspecifics. Neural activity was determined by measuring expression level of the immediate-early-gene (IEG) protein Zenk. Crows displayed aggressive and/or submissive behaviour to opponents less frequently but more discriminatively in subsequent encounters, suggesting stable dominance based on memory, including win/loss outcomes of the first encounters and individual discrimination. Neural correlates of aggressive and submissive behaviour were found in limbic subpallium including septum, bed nucleus of the striae terminalis (BST), and nucleus taeniae of amygdala (TnA), but also those to familiarity factor in BST and TnA. Contrastingly, correlates of social behaviour were little in pallium and those of familiarity with exposed individuals were identified in hippocampus, medial meso-/nidopallium, and ventro-caudal nidopallium. Given the anatomical connection and neural response patterns of the focal nuclei, neural networks connecting pallium and limbic subpallium via hippocampus could be involved in the integration of individual discrimination and social behaviour control in memory-based dominance in the crow.

  6. Neural Networks

    DTIC Science & Technology

    1990-01-01

    FUNDING NUMBERS PROGRAM PROJECT TASK WORK UNIT ELEMENT NO. NO. NO. ACCESSION NO 11 TITLE (Include Security Classification) NEURAL NETWORKS 12. PERSONAL...SUB-GROUP Neural Networks Optical Architectures Nonlinear Optics Adaptation 19. ABSTRACT (Continue on reverse if necessary and identify by block number...341i Y C-odes , lo iii/(iv blank) 1. INTRODUCTION Neural networks are a type of distributed processing system [1

  7. Neural Networks

    SciTech Connect

    Smith, Patrick I.

    2003-09-23

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  8. Search for standard model Higgs boson production in association with a W boson using a neural network discriminant at CDF

    SciTech Connect

    Aaltonen, T.; Maki, T.; Mehtala, P.; Orava, R.; Osterberg, K.; Saarikko, H.; Remortel, N. van; Adelman, J.; Brubaker, E.; Fedorko, W. T.; Grosso-Pilcher, C.; Ketchum, W.; Kim, Y. K.; Krop, D.; Kwang, S.; Lee, H. S.; Paramonov, A. A.; Schmidt, M. A.; Shiraishi, S.; Shochet, M.

    2009-07-01

    We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions (pp{yields}W{sup {+-}}H{yields}l{nu}bb) at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 fb{sup -1}. We select events consistent with a signature of a single charged lepton (e{sup {+-}}/{mu}{sup {+-}}), missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to 150 GeV/c{sup 2}, respectively.

  9. Search for standard model Higgs boson production in association with a W boson using a neural network discriminant at CDF

    NASA Astrophysics Data System (ADS)

    Aaltonen, T.; Adelman, J.; Akimoto, T.; González, B. Álvarez; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Apresyan, A.; Arisawa, T.; Artikov, A.; Ashmanskas, W.; Attal, A.; Aurisano, A.; Azfar, F.; Badgett, W.; Barbaro-Galtieri, A.; Barnes, V. E.; Barnett, B. A.; Barria, P.; Bartsch, V.; Bauer, G.; Beauchemin, P.-H.; Bedeschi, F.; Beecher, D.; Behari, S.; Bellettini, G.; Bellinger, J.; Benjamin, D.; Beretvas, A.; Beringer, J.; Bhatti, A.; Binkley, M.; Bisello, D.; Bizjak, I.; Blair, R. E.; Blocker, C.; Blumenfeld, B.; Bocci, A.; Bodek, A.; Boisvert, V.; Bolla, G.; Bortoletto, D.; Boudreau, J.; Boveia, A.; Brau, B.; Bridgeman, A.; Brigliadori, L.; Bromberg, C.; Brubaker, E.; Budagov, J.; Budd, H. S.; Budd, S.; Burke, S.; Burkett, K.; Busetto, G.; Bussey, P.; Buzatu, A.; Byrum, K. L.; Cabrera, S.; Calancha, C.; Campanelli, M.; Campbell, M.; Canelli, F.; Canepa, A.; Carls, B.; Carlsmith, D.; Carosi, R.; Carrillo, S.; Carron, S.; Casal, B.; Casarsa, M.; Castro, A.; Catastini, P.; Cauz, D.; Cavaliere, V.; Cavalli-Sforza, M.; Cerri, A.; Cerrito, L.; Chang, S. H.; Chen, Y. C.; Chertok, M.; Chiarelli, G.; Chlachidze, G.; Chlebana, F.; Cho, K.; Chokheli, D.; Chou, J. P.; Choudalakis, G.; Chuang, S. H.; Chung, K.; Chung, W. H.; Chung, Y. S.; Chwalek, T.; Ciobanu, C. I.; Ciocci, M. A.; Clark, A.; Clark, D.; Compostella, G.; Convery, M. E.; Conway, J.; Cordelli, M.; Cortiana, G.; Cox, C. A.; Cox, D. J.; Crescioli, F.; Almenar, C. Cuenca; Cuevas, J.; Culbertson, R.; Cully, J. C.; Dagenhart, D.; Datta, M.; Davies, T.; de Barbaro, P.; de Cecco, S.; Deisher, A.; de Lorenzo, G.; Dell'Orso, M.; Deluca, C.; Demortier, L.; Deng, J.; Deninno, M.; Derwent, P. F.; di Canto, A.; di Giovanni, G. P.; Dionisi, C.; di Ruzza, B.; Dittmann, J. R.; D'Onofrio, M.; Donati, S.; Dong, P.; Donini, J.; Dorigo, T.; Dube, S.; Efron, J.; Elagin, A.; Erbacher, R.; Errede, D.; Errede, S.; Eusebi, R.; Fang, H. C.; Farrington, S.; Fedorko, W. T.; Feild, R. G.; Feindt, M.; Fernandez, J. P.; Ferrazza, C.; Field, R.; Flanagan, G.; Forrest, R.; Frank, M. J.; Franklin, M.; Freeman, J. C.; Furic, I.; Gallinaro, M.; Galyardt, J.; Garberson, F.; Garcia, J. E.; Garfinkel, A. F.; Garosi, P.; Genser, K.; Gerberich, H.; Gerdes, D.; Gessler, A.; Giagu, S.; Giakoumopoulou, V.; Giannetti, P.; Gibson, K.; Gimmell, J. L.; Ginsburg, C. M.; Giokaris, N.; Giordani, M.; Giromini, P.; Giunta, M.; Giurgiu, G.; Glagolev, V.; Glenzinski, D.; Gold, M.; Goldschmidt, N.; Golossanov, A.; Gomez, G.; Gomez-Ceballos, G.; Goncharov, M.; González, O.; Gorelov, I.; Goshaw, A. T.; Goulianos, K.; Gresele, A.; Grinstein, S.; Grosso-Pilcher, C.; Group, R. C.; Grundler, U.; da Costa, J. Guimaraes; Gunay-Unalan, Z.; Haber, C.; Hahn, K.; Hahn, S. R.; Halkiadakis, E.; Han, B.-Y.; Han, J. Y.; Happacher, F.; Hara, K.; Hare, D.; Hare, M.; Harper, S.; Harr, R. F.; Harris, R. M.; Hartz, M.; Hatakeyama, K.; Hays, C.; Heck, M.; Heijboer, A.; Heinrich, J.; Henderson, C.; Herndon, M.; Heuser, J.; Hewamanage, S.; Hidas, D.; Hill, C. S.; Hirschbuehl, D.; Hocker, A.; Hou, S.; Houlden, M.; Hsu, S.-C.; Huffman, B. T.; Hughes, R. E.; Husemann, U.; Hussein, M.; Huston, J.; Incandela, J.; Introzzi, G.; Iori, M.; Ivanov, A.; James, E.; Jang, D.; Jayatilaka, B.; Jeon, E. J.; Jha, M. K.; Jindariani, S.; Johnson, W.; Jones, M.; Joo, K. K.; Jun, S. Y.; Jung, J. E.; Junk, T. R.; Kamon, T.; Kar, D.; Karchin, P. E.; Kato, Y.; Kephart, R.; Ketchum, W.; Keung, J.; Khotilovich, V.; Kilminster, B.; Kim, D. H.; Kim, H. S.; Kim, H. W.; Kim, J. E.; Kim, M. J.; Kim, S. B.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kirsch, L.; Klimenko, S.; Knuteson, B.; Ko, B. R.; Kondo, K.; Kong, D. J.; Konigsberg, J.; Korytov, A.; Kotwal, A. V.; Kreps, M.; Kroll, J.; Krop, D.; Krumnack, N.; Kruse, M.; Krutelyov, V.; Kubo, T.; Kuhr, T.; Kulkarni, N. P.; Kurata, M.; Kwang, S.; Laasanen, A. T.; Lami, S.; Lammel, S.; Lancaster, M.; Lander, R. L.; Lannon, K.; Lath, A.; Latino, G.; Lazzizzera, I.; Lecompte, T.; Lee, E.; Lee, H. S.; Lee, S. W.; Leone, S.; Lewis, J. D.; Lin, C.-S.; Linacre, J.; Lindgren, M.; Lipeles, E.; Lister, A.; Litvintsev, D. O.; Liu, C.; Liu, T.; Lockyer, N. S.; Loginov, A.; Loreti, M.; Lovas, L.; Lucchesi, D.; Luci, C.; Lueck, J.; Lujan, P.; Lukens, P.; Lungu, G.; Lyons, L.; Lys, J.; Lysak, R.; MacQueen, D.; Madrak, R.; Maeshima, K.; Makhoul, K.; Maki, T.; Maksimovic, P.; Malde, S.; Malik, S.; Manca, G.; Manousakis-Katsikakis, A.; Margaroli, F.; Marino, C.; Marino, C. P.; Martin, A.; Martin, V.; Martínez, M.; Martínez-Ballarín, R.; Maruyama, T.; Mastrandrea, P.; Masubuchi, T.; Mathis, M.; Mattson, M. E.; Mazzanti, P.; McFarland, K. S.; McIntyre, P.; McNulty, R.; Mehta, A.; Mehtala, P.; Menzione, A.; Merkel, P.; Mesropian, C.; Miao, T.; Miladinovic, N.; Miller, R.; Mills, C.; Milnik, M.; Mitra, A.; Mitselmakher, G.; Miyake, H.; Moggi, N.; Mondragon, M. N.; Moon, C. S.; Moore, R.; Morello, M. J.; Morlock, J.; Fernandez, P. Movilla; Mülmenstädt, J.; Mukherjee, A.; Muller, Th.; Mumford, R.; Murat, P.; Mussini, M.; Nachtman, J.; Nagai, Y.; Nagano, A.; Naganoma, J.; Nakamura, K.; Nakano, I.; Napier, A.; Necula, V.; Nett, J.; Neu, C.; Neubauer, M. S.; Neubauer, S.; Nielsen, J.; Nodulman, L.; Norman, M.; Norniella, O.; Nurse, E.; Oakes, L.; Oh, S. H.; Oh, Y. D.; Oksuzian, I.; Okusawa, T.; Orava, R.; Osterberg, K.; Griso, S. Pagan; Palencia, E.; Papadimitriou, V.; Papaikonomou, A.; Paramonov, A. A.; Parks, B.; Pashapour, S.; Patrick, J.; Pauletta, G.; Paulini, M.; Paus, C.; Peiffer, T.; Pellett, D. E.; Penzo, A.; Phillips, T. J.; Piacentino, G.; Pianori, E.; Pinera, L.; Pitts, K.; Plager, C.; Pondrom, L.; Poukhov, O.; Pounder, N.; Prakoshyn, F.; Pronko, A.; Proudfoot, J.; Ptohos, F.; Pueschel, E.; Punzi, G.; Pursley, J.; Rademacker, J.; Rahaman, A.; Ramakrishnan, V.; Ranjan, N.; Redondo, I.; Renton, P.; Renz, M.; Rescigno, M.; Richter, S.; Rimondi, F.; Ristori, L.; Robson, A.; Rodrigo, T.; Rodriguez, T.; Rogers, E.; Rolli, S.; Roser, R.; Rossi, M.; Rossin, R.; Roy, P.; Ruiz, A.; Russ, J.; Rusu, V.; Rutherford, B.; Saarikko, H.; Safonov, A.; Sakumoto, W. K.; Saltó, O.; Santi, L.; Sarkar, S.; Sartori, L.; Sato, K.; Savoy-Navarro, A.; Schlabach, P.; Schmidt, A.; Schmidt, E. E.; Schmidt, M. A.; Schmidt, M. P.; Schmitt, M.; Schwarz, T.; Scodellaro, L.; Scribano, A.; Scuri, F.; Sedov, A.; Seidel, S.; Seiya, Y.; Semenov, A.; Sexton-Kennedy, L.; Sforza, F.; Sfyrla, A.; Shalhout, S. Z.; Shears, T.; Shepard, P. F.; Shimojima, M.; Shiraishi, S.; Shochet, M.; Shon, Y.; Shreyber, I.; Sinervo, P.; Sisakyan, A.; Slaughter, A. J.; Slaunwhite, J.; Sliwa, K.; Smith, J. R.; Snider, F. D.; Snihur, R.; Soha, A.; Somalwar, S.; Sorin, V.; Spreitzer, T.; Squillacioti, P.; Stanitzki, M.; St. Denis, R.; Stelzer, B.; Stelzer-Chilton, O.; Stentz, D.; Strologas, J.; Strycker, G. L.; Suh, J. S.; Sukhanov, A.; Suslov, I.; Suzuki, T.; Taffard, A.; Takashima, R.; Takeuchi, Y.; Tanaka, R.; Tecchio, M.; Teng, P. K.; Terashi, K.; Thom, J.; Thompson, A. S.; Thompson, G. A.; Thomson, E.; Tipton, P.; Ttito-Guzmán, P.; Tkaczyk, S.; Toback, D.; Tokar, S.; Tollefson, K.; Tomura, T.; Tonelli, D.; Torre, S.; Torretta, D.; Totaro, P.; Tourneur, S.; Trovato, M.; Tsai, S.-Y.; Tu, Y.; Turini, N.; Ukegawa, F.; Vallecorsa, S.; van Remortel, N.; Varganov, A.; Vataga, E.; Vázquez, F.; Velev, G.; Vellidis, C.; Vidal, M.; Vidal, R.; Vila, I.; Vilar, R.; Vine, T.; Vogel, M.; Volobouev, I.; Volpi, G.; Wagner, P.; Wagner, R. G.; Wagner, R. L.; Wagner, W.; Wagner-Kuhr, J.; Wakisaka, T.; Wallny, R.; Wang, S. M.; Warburton, A.; Waters, D.; Weinberger, M.; Weinelt, J.; Wester, W. C., III; Whitehouse, B.; Whiteson, D.; Wicklund, A. B.; Wicklund, E.; Wilbur, S.; Williams, G.; Williams, H. H.; Wilson, P.; Winer, B. L.; Wittich, P.; Wolbers, S.; Wolfe, C.; Wright, T.; Wu, X.; Würthwein, F.; Xie, S.; Yagil, A.; Yamamoto, K.; Yamaoka, J.; Yang, U. K.; Yang, Y. C.; Yao, W. M.; Yeh, G. P.; Yi, K.; Yoh, J.; Yorita, K.; Yoshida, T.; Yu, G. B.; Yu, I.; Yu, S. S.; Yun, J. C.; Zanello, L.; Zanetti, A.; Zhang, X.; Zheng, Y.; Zucchelli, S.

    2009-07-01

    We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions (p pmacr →W±H→ℓνb bmacr ) at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9fb-1. We select events consistent with a signature of a single charged lepton (e±/μ±), missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to 150GeV/c2, respectively.

  10. Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops

    PubMed Central

    de Castro, Ana-Isabel; Jurado-Expósito, Montserrat; Gómez-Casero, María-Teresa; López-Granados, Francisca

    2012-01-01

    In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops. PMID:22629171

  11. Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops.

    PubMed

    de Castro, Ana-Isabel; Jurado-Expósito, Montserrat; Gómez-Casero, María-Teresa; López-Granados, Francisca

    2012-01-01

    In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.

  12. [Improved learning capacity and discrimination performance of neural networks in pattern recognition of biosignals].

    PubMed

    Herrmann, L; Rienäcker, U

    1992-04-01

    Pattern recognition was an important goal in the early work on artificial neural networks. Without arousing dramatic speculation, the paper describes, how a "natural" method of dealing with the configuration of the input layer can considerably improve learning behaviour and classification rate of a modified multi-layered perception with backpropagation of the error learning rule. Using this method, recognition of complex patterns in electrophysiological signals can be performed more accurately, without rules or complicated heuristic procedures. The proposed technique is demonstrated using recognition of the J-point in the ECG as an example.

  13. Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression

    NASA Astrophysics Data System (ADS)

    Mousavi, S. Mostafa; Horton, Stephen P.; Langston, Charles A.; Samei, Borhan

    2016-10-01

    We develop an automated strategy for discriminating deep microseismic events from shallow ones on the basis of the waveforms recorded on a limited number of surface receivers. Machine-learning techniques are employed to explore the relationship between event hypocentres and seismic features of the recorded signals in time, frequency and time-frequency domains. We applied the technique to 440 microearthquakes -1.7 < Mw < 1.29, induced by an underground cavern collapse in the Napoleonville Salt Dome in Bayou Corne, Louisiana. Forty different seismic attributes of whole seismograms including degree of polarization and spectral attributes were measured. A selected set of features was then used to train the system to discriminate between deep and shallow events based on the knowledge gained from existing patterns. The cross-validation test showed that events with depth shallower than 250 m can be discriminated from events with hypocentral depth between 1000 and 2000 m with 88 per cent and 90.7 per cent accuracy using logistic regression and artificial neural network models, respectively. Similar results were obtained using single station seismograms. The results show that the spectral features have the highest correlation to source depth. Spectral centroids and 2-D cross-correlations in the time-frequency domain are two new seismic features used in this study that showed to be promising measures for seismic event classification. The used machine-learning techniques have application for efficient automatic classification of low energy signals recorded at one or more seismic stations.

  14. Discrimination of Rare +circ and +circ Decays at Rest Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Frlež, Emil

    2007-04-01

    PEN, a new experiment aiming to measure the +circ->e^+ν decay branching ratio (πe2) with a relative uncertainty of ˜5.10-4 is being mounted at the Paul Scherrer Institute ring accelerator. The PIBETA detector was upgraded with new active degrader and fast active target detectors. Low-momentum +circ beam tunes in the range 72-80,eV/c were developed producing stopping rates of up to 20k,+circ/s. Beam detector PMT waveforms were recorded with a 2,Hz/10 bit Acqiris digitizer. The ``system waveform functions'' for the stopping +circ's and +circ's and decaying e^+'s were determined from a set of clean Michel events. The +circ->e^+ν events were distinguished from the copious Michel background using artificial neural network analysis.

  15. Identification and Discrimination of Brands of Fuels by Gas Chromatography and Neural Networks Algorithm in Forensic Research

    PubMed Central

    Ugena, L.; Moncayo, S.; Manzoor, S.; Rosales, D.

    2016-01-01

    The detection of adulteration of fuels and its use in criminal scenes like arson has a high interest in forensic investigations. In this work, a method based on gas chromatography (GC) and neural networks (NN) has been developed and applied to the identification and discrimination of brands of fuels such as gasoline and diesel without the necessity to determine the composition of the samples. The study included five main brands of fuels from Spain, collected from fifteen different local petrol stations. The methodology allowed the identification of the gasoline and diesel brands with a high accuracy close to 100%, without any false positives or false negatives. A success rate of three blind samples was obtained as 73.3%, 80%, and 100%, respectively. The results obtained demonstrate the potential of this methodology to help in resolving criminal situations. PMID:27375919

  16. Optical Neural Network Classifier Architectures

    DTIC Science & Technology

    1998-04-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and...function neural network based on a previously demonstrated binary-input version. The greyscale-input capability broadens the range of applications for...a reduced feature set of multiwavelet images to improve training times and discrimination capability of the neural network . The design uses a joint

  17. Neural Networks

    NASA Astrophysics Data System (ADS)

    Schwindling, Jerome

    2010-04-01

    This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p.) corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  18. Neural Network Function Classifier

    DTIC Science & Technology

    2003-02-07

    neural network sets. Each of the neural networks in a particular set is trained to recognize a particular data set type. The best function representation of the data set is determined from the neural network output. The system comprises sets of trained neural networks having neural networks trained to identify different types of data. The number of neural networks within each neural network set will depend on the number of function types that are represented. The system further comprises

  19. Discrimination of volcano activity and mountain-associated waves using infrasonic data and a backpropagation neural network

    NASA Astrophysics Data System (ADS)

    Ham, Fredric M.; Leeney, Thomas A.; Canady, Heather M.; Wheeler, Joseph C.

    1999-03-01

    An integral part of the Comprehensive Nuclear Test Ban Treaty monitoring is an international infrasonic monitoring network that is capable of detecting and verifying nuclear explosions. Reliable detection of such events must be made from data that may contain other sources of infrasonic phenomena. Infrasonic waves can also result from volcanic eruptions, mountain associated waves, auroral waves, earthquakes, meteors, avalanches, severe weather, quarry blasting, high-speed aircraft, gravity waves, and microbaroms. This paper shows that a feedforward multi-layer neural network discriminator, trained by backpropagation, is capable of distinguishing between two unique infrasonic events recorded from single station recordings with a relatively high degree of accuracy. The two types of infrasonic events used in this study are volcanic eruptions and a set of mountain associated waves recorded at Windless Bight, Antarctica. An important element for the successful classification of infrasonic events is the preprocessing techniques used to form a set of feature vectors that can be used to train and test the neural network. The preprocessing steps used in our analysis for the infrasonic data are similar to those techniques used in speech processing, specifically speech recognition. From the raw time-domain infrasonic data, a set of mel-frequency cepstral coefficients and their associated derivatives for each signal are used to form, a set of feature vectors. These feature vectors contain the pertinent characteristics of the data that can be used to classify the events of interest as opposed to using the raw data. A linear analysis was first performed on the feature vector space to determine the best combination of mel-frequency cepstral coefficients and derivatives. Then several simulations were run to distinguish between two different volcanic events, and mountain associated waves versus volcanic events, using their infrasonic characteristics.

  20. Single-base-pair discrimination of terminal mismatches by using oligonucleotide microarrays and neural network analyses

    NASA Technical Reports Server (NTRS)

    Urakawa, Hidetoshi; Noble, Peter A.; El Fantroussi, Said; Kelly, John J.; Stahl, David A.

    2002-01-01

    The effects of single-base-pair near-terminal and terminal mismatches on the dissociation temperature (T(d)) and signal intensity of short DNA duplexes were determined by using oligonucleotide microarrays and neural network (NN) analyses. Two perfect-match probes and 29 probes having a single-base-pair mismatch at positions 1 to 5 from the 5' terminus of the probe were designed to target one of two short sequences representing 16S rRNA. Nonequilibrium dissociation rates (i.e., melting profiles) of all probe-target duplexes were determined simultaneously. Analysis of variance revealed that position of the mismatch, type of mismatch, and formamide concentration significantly affected the T(d) and signal intensity. Increasing the concentration of formamide in the washing buffer decreased the T(d) and signal intensity, and it decreased the variability of the signal. Although T(d)s of probe-target duplexes with mismatches in the first or second position were not significantly different from one another, duplexes with mismatches in the third to fifth positions had significantly lower T(d)s than those with mismatches in the first or second position. The trained NNs predicted the T(d) with high accuracies (R(2) = 0.93). However, the NNs predicted the signal intensity only moderately accurately (R(2) = 0.67), presumably due to increased noise in the signal intensity at low formamide concentrations. Sensitivity analysis revealed that the concentration of formamide explained most (75%) of the variability in T(d)s, followed by position of the mismatch (19%) and type of mismatch (6%). The results suggest that position of the mismatch at or near the 5' terminus plays a greater role in determining the T(d) and signal intensity of duplexes than the type of mismatch.

  1. Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

    NASA Astrophysics Data System (ADS)

    Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

    2015-06-01

    Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

  2. Classification of observational data with artificial neural networks versus discriminant analysis in pharmacoepidemiological studies--can outcome of fluoxetine treatment be predicted?

    PubMed

    Winterer, G; Ziller, M; Linden, M

    1998-11-01

    For several years, there has been an ongoing discussion about appropriate methodological tools to be applied to observational data in pharmacoepidemiological studies. It is now suggested by our research group that artificial neural networks (ANN) might be advantageous in some cases for classification purposes when compared with discriminant analysis. This is due to their inherent capability to detect complex linear and nonlinear functions in multivariate data sets, the possibility of including data on different scales in the same model, as well as their relative resistance to "noisy" input. In this paper, a short introduction is given to the basics of neural networks and possible applications. For demonstration, a comparison between artificial neural networks and discriminant analysis was performed on a multivariate data set, consisting of observational data of 19738 patients treated with fluoxetine. It was tested, which of the two statistical tools outperforms the two other in regard to the therapeutic response prediction from the clinical input data. Essentially, it was found that neither discriminant analysis nor ANN are able to predict the clinical outcome on the basis of the employed clinical variables. Applying ANN, we were able to rule out the possibility of undetected suppressor effects to a greater extent than would have been possible by the exclusive application of discriminant analysis.

  3. Highly sensitive detection and discrimination of LR and YR microcystins based on protein phosphatases and an artificial neural network.

    PubMed

    Covaci, O I; Sassolas, A; Alonso, G A; Muñoz, R; Radu, G L; Bucur, B; Marty, J-L

    2012-08-01

    The inhibition characteristics of three different protein phosphatases by three microcystin (MC) variants--LR, YR, and RR--were studied. The corresponding K (I) for each enzyme-MC couple was calculated. The toxicity of MC varies in the following order: MC-LR > MC-YR > MC-RR. The sensitivity of the enzymes increased in the following order: mutant PP2A < mutant PP1 < natural PP2A. The best limit of detection obtained was 21.2 pM MC-LR using the most sensible enzyme. Methanol, ethanol, and acetonitrile up to 2 % (v/v) may be used in inhibition measurements. An artificial neural network (ANN) was used to discriminate two MC variants--LR and YR--using the differences in inhibition percentages measured with mutant PP1 and natural PP2A. The ANN is able to analyze mixtures with concentrations ranging from 8 to 98 pM MC-LR and 31 to 373 pM MC-YR.

  4. Neural Network Studies

    DTIC Science & Technology

    1993-07-01

    basic useful theorems and general rules which apply to neural networks (in ’Overview of Neural Network Theory’), studies of training time as the...The Neural Network , Bayes- Gaussian, and k-Nearest Neighbor Classifiers’), an analysis of fuzzy logic and its relationship to neural network (in ’Fuzzy

  5. Electronic Neural Networks

    NASA Technical Reports Server (NTRS)

    Thakoor, Anil

    1990-01-01

    Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

  6. Discrimination between methicillin-resistant and methicillin-susceptible Staphylococcus aureus using pyrolysis mass spectrometry and artificial neural networks.

    PubMed

    Goodacre, R; Rooney, P J; Kell, D B

    1998-01-01

    Curie-point pyrolysis mass spectra were obtained from 15 methicillin-resistant and 22 methicillin-susceptible Staphylococcus aureus strains. Cluster analysis showed that the major source of variation between the pyrolysis mass spectra resulted from the phage group of the bacteria, not their resistance or susceptibility to methicillin. By contrast, artificial neural networks could be trained to recognize those aspects of the pyrolysis mass spectra that differentiated methicillin-resistant from methicillin-sensitive strains. The trained neural network could then use pyrolysis mass spectral data to assess whether an unknown strain was resistant to methicillin. These results give the first demonstration that the combination of pyrolysis mass spectrometry with neural networks can provide a very rapid and accurate antibiotic susceptibility testing technique.

  7. Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

    NASA Astrophysics Data System (ADS)

    Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

    2016-10-01

    The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

  8. Generalized classifier neural network.

    PubMed

    Ozyildirim, Buse Melis; Avci, Mutlu

    2013-03-01

    In this work a new radial basis function based classification neural network named as generalized classifier neural network, is proposed. The proposed generalized classifier neural network has five layers, unlike other radial basis function based neural networks such as generalized regression neural network and probabilistic neural network. They are input, pattern, summation, normalization and output layers. In addition to topological difference, the proposed neural network has gradient descent based optimization of smoothing parameter approach and diverge effect term added calculation improvements. Diverge effect term is an improvement on summation layer calculation to supply additional separation ability and flexibility. Performance of generalized classifier neural network is compared with that of the probabilistic neural network, multilayer perceptron algorithm and radial basis function neural network on 9 different data sets and with that of generalized regression neural network on 3 different data sets include only two classes in MATLAB environment. Better classification performance up to %89 is observed. Improved classification performances proved the effectivity of the proposed neural network.

  9. Nonlinear Neural Network Oscillator.

    DTIC Science & Technology

    A nonlinear oscillator (10) includes a neural network (12) having at least one output (12a) for outputting a one dimensional vector. The neural ... neural network and the input of the input layer for modifying a magnitude and/or a polarity of the one dimensional output vector prior to the sample of...first or a second direction. Connection weights of the neural network are trained on a deterministic sequence of data from a chaotic source or may be a

  10. Neural Network Hurricane Tracker

    DTIC Science & Technology

    1998-05-27

    data about the hurricane and supplying the data to a trained neural network for yielding a predicted path for the hurricane. The system further includes...a device for displaying the predicted path of the hurricane. A method for using and training the neural network in the system is described. In the...method, the neural network is trained using information about hurricanes in a specific geographical area maintained in a database. The training involves

  11. Fast identification of biominerals by means of stand-off laser-induced breakdown spectroscopy using linear discriminant analysis and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Vítková, Gabriela; Novotný, Karel; Prokeš, Lubomír; Hrdlička, Aleš; Kaiser, Jozef; Novotný, Jan; Malina, Radomír; Prochazka, David

    2012-07-01

    The goal of this paper is to compare two selected statistical techniques used for identification of archeological materials merely on the base of their spectra obtained by stand-off laser-induced breakdown spectroscopy (stand-off LIBS). Data processing using linear discriminant analysis (LDA) and artificial neural networks (ANN) were applied on spectra of 18 different samples, some of them archeological and some recent, containing 7 types of material (i.e. shells, mortar, bricks, soil pellets, ceramic, teeth and bones). As the input data PCA scores were taken. The intended aim of this work is to create a database for simple and fast identification of archeological or paleontological materials in situ. This approach can speed up and simplify the sampling process during archeological excavations that nowadays tend to be quite damaging and time-consuming.

  12. Studies in Neural Networks

    DTIC Science & Technology

    1991-01-01

    N00014-87-K-0377 TITLE: "Studies in Neural Networks " fl.U Q l~~izie JUL 021991 "" " F.: L9’CO37 "I! c-1(.d Contract No.: N00014-87-K-0377 Final...34) have been very useful, both in understanding the dynamics of neural networks and in engineering networks to perform particular tasks. We have noted...understanding more complex network computation. Interest in applying ideas from biological neural networks to real problems of engineering raises the issues of

  13. Using machine learning to classify image features from canine pelvic radiographs: evaluation of partial least squares discriminant analysis and artificial neural network models.

    PubMed

    McEvoy, Fintan J; Amigo, José M

    2013-01-01

    As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine hip joint on ventrodorsal pelvis radiographs. A training set of images (120 of the hip and 80 from other regions) was used to train a linear partial least squares discriminant analysis (PLS-DA) model and a nonlinear artificial neural network (ANN) model to classify hip images. Performance of the models was assessed using a separate test image set (36 containing hips and 20 from other areas). Partial least squares discriminant analysis model achieved a classification error, sensitivity, and specificity of 6.7%, 100%, and 89%, respectively. The corresponding values for the ANN model were 8.9%, 86%, and 100%. Findings indicated that statistical classification of veterinary images is feasible and has the potential for grouping and classifying images or image features, especially when a large number of well-classified images are available for model training.

  14. Application of effective wavelengths and BP neural network for the discrimination of varieties of instant milk tea powders using visible and near infrared spectroscopy

    NASA Astrophysics Data System (ADS)

    Liu, Fei; He, Yong; Wang, Li

    2007-11-01

    In order to implement the fast discrimination of different milk tea powders with different internal qualities, visible and near infrared (Vis/NIR) spectroscopy combined with effective wavelengths (EWs) and BP neural network (BPNN) was investigated as a new approach. Five brands of milk teas were obtained and 225 samples were selected randomly for the calibration set, while 75 samples for the validation set. The EWs were selected according to x-loading weights and regression coefficients by PLS analysis after some preprocessing. A total of 18 EWs (400, 401, 452, 453, 502, 503, 534, 535, 594, 595, 635, 636, 688, 689, 987, 988, 995 and 996 nm) were selected as the inputs of BPNN model. The performance was validated by the calibration and validation sets. The threshold error of prediction was set as +/-0.1 and an excellent precision and recognition ratio of 100% for calibration set and 98.7% for validation set were achieved. The prediction results indicated that the EWs reflected the main characteristics of milk tea of different brands based on Vis/NIR spectroscopy and BPNN model, and the EWs would be useful for the development of portable instrument to discriminate the variety and detect the adulteration of instant milk tea powders.

  15. Probabilistic Analysis of Neural Networks

    DTIC Science & Technology

    1990-11-26

    provide an understanding of the basic mechanisms of learning and recognition in neural networks . The main areas of progress were analysis of neural ... networks models, study of network connectivity, and investigation of computer network theory.

  16. Neural networks for aircraft control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis

    1990-01-01

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

  17. Rapid neural discrimination of communicative gestures.

    PubMed

    Redcay, Elizabeth; Carlson, Thomas A

    2015-04-01

    Humans are biased toward social interaction. Behaviorally, this bias is evident in the rapid effects that self-relevant communicative signals have on attention and perceptual systems. The processing of communicative cues recruits a wide network of brain regions, including mentalizing systems. Relatively less work, however, has examined the timing of the processing of self-relevant communicative cues. In the present study, we used multivariate pattern analysis (decoding) approach to the analysis of magnetoencephalography (MEG) to study the processing dynamics of social-communicative actions. Twenty-four participants viewed images of a woman performing actions that varied on a continuum of communicative factors including self-relevance (to the participant) and emotional valence, while their brain activity was recorded using MEG. Controlling for low-level visual factors, we found early discrimination of emotional valence (70 ms) and self-relevant communicative signals (100 ms). These data offer neural support for the robust and rapid effects of self-relevant communicative cues on behavior.

  18. Critical Branching Neural Networks

    ERIC Educational Resources Information Center

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  19. Neural Networks: A Primer

    DTIC Science & Technology

    1991-05-01

    capture underlying relationships directly from observed behavior is one of the primary capabilities of neural networks. 29 Back P’ropagation Approximailon...model complex behavior patterns. Particularly in areas traditionally addressed by regression and other functional based techniques, neural networks...to.be determined directly from the observed behavior of a system or sample of individuals. This ability should prove important in personnel analysis and

  20. Texture analysis of T1- and T2-weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children

    PubMed Central

    Orphanidou-Vlachou, Eleni; Vlachos, Nikolaos; Davies, Nigel P; Arvanitis, Theodoros N; Grundy, Richard G; Peet, Andrew C

    2014-01-01

    Brain tumours are the most common solid tumours in children, representing 20% of all cancers. The most frequent posterior fossa tumours are medulloblastomas, pilocytic astrocytomas and ependymomas. Texture analysis (TA) of MR images can be used to support the diagnosis of these tumours by providing additional quantitative information. MaZda software was used to perform TA on T1- and T2-weighted images of children with pilocytic astrocytomas, medulloblastomas and ependymomas of the posterior fossa, who had MRI at Birmingham Children's Hospital prior to treatment. The region of interest was selected on three slices per patient in Image J, using thresholding and manual outlining. TA produced 279 features, which were reduced using principal component analysis (PCA). The principal components (PCs) explaining 95% of the variance were used in a linear discriminant analysis (LDA) and a probabilistic neural network (PNN) to classify the cases, using DTREG statistics software. PCA of texture features from both T1- and T2-weighted images yielded 13 PCs to explain >95% of the variance. The PNN classifier for T1-weighted images achieved 100% accuracy on training the data and 90% on leave-one-out cross-validation (LOOCV); for T2-weighted images, the accuracy was 100% on training the data and 93.3% on LOOCV. A PNN classifier with T1 and T2 PCs achieved 100% accuracy on training the data and 85.8% on LOOCV. LDA classification accuracies were noticeably poorer. The features found to hold the highest discriminating potential were all co-occurrence matrix derived, where adjacent pixels had highly correlated intensities. This study shows that TA can be performed on standard T1- and T2-weighted images of childhood posterior fossa tumours using readily available software to provide high diagnostic accuracy. Discriminatory features do not correspond to those used in the clinical interpretation of the images and therefore provide novel tumour information. Copyright © 2014 John Wiley

  1. Programming neural networks

    SciTech Connect

    Anderson, J.A.; Markman, A.B.; Viscuso, S.R.; Wisniewski, E.J.

    1988-09-01

    Neural networks ''compute'' though not in the way that traditional computers do. One must accept their weaknesses to use their strengths. The authors present several applications of a particular non-linear network (the BSB model) to illustrate some of the peculiarities inherent in this architecture.

  2. Tomography using neural networks

    NASA Astrophysics Data System (ADS)

    Demeter, G.

    1997-03-01

    We have utilized neural networks for fast evaluation of tomographic data on the MT-1M tokamak. The networks have proven useful in providing the parameters of a nonlinear fit to experimental data, producing results in a fraction of the time required for performing the nonlinear fit. Time required for training the networks makes the method worth applying only if a substantial amount of data are to be evaluated.

  3. The Adaptive Kernel Neural Network

    DTIC Science & Technology

    1989-10-01

    A neural network architecture for clustering and classification is described. The Adaptive Kernel Neural Network (AKNN) is a density estimation...classification layer. The AKNN retains the inherent parallelism common in neural network models. Its relationship to the kernel estimator allows the network to

  4. Hyperbolic Hopfield neural networks.

    PubMed

    Kobayashi, M

    2013-02-01

    In recent years, several neural networks using Clifford algebra have been studied. Clifford algebra is also called geometric algebra. Complex-valued Hopfield neural networks (CHNNs) are the most popular neural networks using Clifford algebra. The aim of this brief is to construct hyperbolic HNNs (HHNNs) as an analog of CHNNs. Hyperbolic algebra is a Clifford algebra based on Lorentzian geometry. In this brief, a hyperbolic neuron is defined in a manner analogous to a phasor neuron, which is a typical complex-valued neuron model. HHNNs share common concepts with CHNNs, such as the angle and energy. However, HHNNs and CHNNs are different in several aspects. The states of hyperbolic neurons do not form a circle, and, therefore, the start and end states are not identical. In the quantized version, unlike complex-valued neurons, hyperbolic neurons have an infinite number of states.

  5. Nested neural networks

    NASA Technical Reports Server (NTRS)

    Baram, Yoram

    1988-01-01

    Nested neural networks, consisting of small interconnected subnetworks, allow for the storage and retrieval of neural state patterns of different sizes. The subnetworks are naturally categorized by layers of corresponding to spatial frequencies in the pattern field. The storage capacity and the error correction capability of the subnetworks generally increase with the degree of connectivity between layers (the nesting degree). Storage of only few subpatterns in each subnetworks results in a vast storage capacity of patterns and subpatterns in the nested network, maintaining high stability and error correction capability.

  6. Neural networks for identifying drunk persons using thermal infrared imagery.

    PubMed

    Koukiou, Georgia; Anastassopoulos, Vassilis

    2015-07-01

    Neural networks were tested on infrared images of faces for discriminating intoxicated persons. The images were acquired during controlled alcohol consumption by forty-one persons. Two different experimental approaches were thoroughly investigated. In the first one, each face was examined, location by location, using each time a different neural network, in order to find out those regions that can be used for discriminating a drunk from a sober person. It was found that it was mainly the face forehead that changed thermal behaviour with alcohol consumption. In the second procedure, a single neural structure was trained on the whole face. The discrimination performance of this neural structure was tested on the same face, as well as on unknown faces. The neural networks presented high discrimination performance even on unknown persons, when trained on the forehead of the sober and the drunk person, respectively. Small neural structures presented better generalisation performance.

  7. Object recognition with hierarchical discriminant saliency networks

    PubMed Central

    Han, Sunhyoung; Vasconcelos, Nuno

    2014-01-01

    The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

  8. Neural networks in psychiatry.

    PubMed

    Hulshoff Pol, Hilleke; Bullmore, Edward

    2013-01-01

    Over the past three decades numerous imaging studies have revealed structural and functional brain abnormalities in patients with neuropsychiatric diseases. These structural and functional brain changes are frequently found in multiple, discrete brain areas and may include frontal, temporal, parietal and occipital cortices as well as subcortical brain areas. However, while the structural and functional brain changes in patients are found in anatomically separated areas, these are connected through (long distance) fibers, together forming networks. Thus, instead of representing separate (patho)-physiological entities, these local changes in the brains of patients with psychiatric disorders may in fact represent different parts of the same 'elephant', i.e., the (altered) brain network. Recent developments in quantitative analysis of complex networks, based largely on graph theory, have revealed that the brain's structure and functions have features of complex networks. Here we briefly introduce several recent developments in neural network studies relevant for psychiatry, including from the 2013 special issue on Neural Networks in Psychiatry in European Neuropsychopharmacology. We conclude that new insights will be revealed from the neural network approaches to brain imaging in psychiatry that hold the potential to find causes for psychiatric disorders and (preventive) treatments in the future.

  9. Neural networks counting chimes.

    PubMed Central

    Amit, D J

    1988-01-01

    It is shown that the ideas that led to neural networks capable of recalling associatively and asynchronously temporal sequences of patterns can be extended to produce a neural network that automatically counts the cardinal number in a sequence of identical external stimuli. The network is explicitly constructed, analyzed, and simulated. Such a network may account for the cognitive effect of the automatic counting of chimes to tell the hour. A more general implication is that different electrophysiological responses to identical stimuli, at certain stages of cortical processing, do not necessarily imply synaptic modification, a la Hebb. Such differences may arise from the fact that consecutive identical inputs find the network in different stages of an active temporal sequence of cognitive states. These types of networks are then situated within a program for the study of cognition, which assigns the detection of meaning as the primary role of attractor neural networks rather than computation, in contrast to the parallel distributed processing attitude to the connectionist project. This interpretation is free of homunculus, as well as from the criticism raised against the cognitive model of symbol manipulation. Computation is then identified as the syntax of temporal sequences of quasi-attractors. PMID:3353371

  10. Evolving Neural Network Pattern Classifiers

    DTIC Science & Technology

    1994-05-01

    This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern...evaluating a multitude of neural network model hypotheses. The evolutionary programming search is augmented with the Solis & Wets random optimization

  11. Mathematical Theory of Neural Networks

    DTIC Science & Technology

    1994-08-31

    This report provides a summary of the grant work by the principal investigators in the area of neural networks . The topics covered deal with...properties) for nets; and the use of neural networks for the control of nonlinear systems.

  12. A neural network architecture for data classification.

    PubMed

    Lezoray, O

    2001-02-01

    This article aims at showing an architecture of neural networks designed for the classification of data distributed among a high number of classes. A significant gain in the global classification rate can be obtained by using our architecture. This latter is based on a set of several little neural networks, each one discriminating only two classes. The specialization of each neural network simplifies their structure and improves the classification. Moreover, the learning step automatically determines the number of hidden neurons. The discussion is illustrated by tests on databases from the UCI machine learning database repository. The experimental results show that this architecture can achieve a faster learning, simpler neural networks and an improved performance in classification.

  13. Neural Network Communications Signal Processing

    DTIC Science & Technology

    1994-08-01

    This final technical report describes the research and development- results of the Neural Network Communications Signal Processing (NNCSP) Program...The objectives of the NNCSP program are to: (1) develop and implement a neural network and communications signal processing simulation system for the...purpose of exploring the applicability of neural network technology to communications signal processing; (2) demonstrate several configurations of the

  14. Neural Networks for Speech Application.

    DTIC Science & Technology

    1987-11-01

    This is a general introduction to the reemerging technology called neural networks , and how these networks may provide an important alternative to...traditional forms of computing in speech applications. Neural networks , sometimes called Artificial Neural Systems (ANS), have shown promise for solving

  15. 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.

  16. Improved Autoassociative Neural Networks

    NASA Technical Reports Server (NTRS)

    Hand, Charles

    2003-01-01

    Improved autoassociative neural networks, denoted nexi, have been proposed for use in controlling autonomous robots, including mobile exploratory robots of the biomorphic type. In comparison with conventional autoassociative neural networks, nexi would be more complex but more capable in that they could be trained to do more complex tasks. A nexus would use bit weights and simple arithmetic in a manner that would enable training and operation without a central processing unit, programs, weight registers, or large amounts of memory. Only a relatively small amount of memory (to hold the bit weights) and a simple logic application- specific integrated circuit would be needed. A description of autoassociative neural networks is prerequisite to a meaningful description of a nexus. An autoassociative network is a set of neurons that are completely connected in the sense that each neuron receives input from, and sends output to, all the other neurons. (In some instantiations, a neuron could also send output back to its own input terminal.) The state of a neuron is completely determined by the inner product of its inputs with weights associated with its input channel. Setting the weights sets the behavior of the network. The neurons of an autoassociative network are usually regarded as comprising a row or vector. Time is a quantized phenomenon for most autoassociative networks in the sense that time proceeds in discrete steps. At each time step, the row of neurons forms a pattern: some neurons are firing, some are not. Hence, the current state of an autoassociative network can be described with a single binary vector. As time goes by, the network changes the vector. Autoassociative networks move vectors over hyperspace landscapes of possibilities.

  17. Neural network technologies

    NASA Technical Reports Server (NTRS)

    Villarreal, James A.

    1991-01-01

    A whole new arena of computer technologies is now beginning to form. Still in its infancy, neural network technology is a biologically inspired methodology which draws on nature's own cognitive processes. The Software Technology Branch has provided a software tool, Neural Execution and Training System (NETS), to industry, government, and academia to facilitate and expedite the use of this technology. NETS is written in the C programming language and can be executed on a variety of machines. Once a network has been debugged, NETS can produce a C source code which implements the network. This code can then be incorporated into other software systems. Described here are various software projects currently under development with NETS and the anticipated future enhancements to NETS and the technology.

  18. Rule generation from neural networks

    SciTech Connect

    Fu, L.

    1994-08-01

    The neural network approach has proven useful for the development of artificial intelligence systems. However, a disadvantage with this approach is that the knowledge embedded in the neural network is opaque. In this paper, we show how to interpret neural network knowledge in symbolic form. We lay down required definitions for this treatment, formulate the interpretation algorithm, and formally verify its soundness. The main result is a formalized relationship between a neural network and a rule-based system. In addition, it has been demonstrated that the neural network generates rules of better performance than the decision tree approach in noisy conditions. 7 refs.

  19. Discrimination of poly(vinyl chloride) samples with different plasticizers and prediction of plasticizer contents in poly(vinyl chloride) using near-infrared spectroscopy and neural-network analysis.

    PubMed

    Saeki, Kazumitsu; Funatsu, Kimito; Tanabe, Kazutoshi

    2003-02-01

    In the recycling of poly(vinyl chloride) (PVC), it is required to discriminate every plasticizer for quality control. For this purpose, the near-infrared spectra were measured for 41 kinds of PVC samples with different plasticizers (DINP, DOP, DOA, TOTM and Polyester) and different plasticizer contents (0-49%). A neural-network analysis was applied to the near-infrared spectra pretreated by second-derivative processing. They were discriminated from one another. The neural-network analysis also allowed us to propose a calibration model which predicts the contents of plasticizers in PVC. The correlation coefficient (R) and the root-mean-square error of prediction (RMSEP) for the DINP calibration model were found to be 0.999 and 0.41 wt%, respectively. In comparison, a partial least-squares regression analysis was carried out. The R and RMSEP of the DINP calibration model were calculated to be 0.993 and 1.27 wt%, respectively. It is found that a near-infrared spectra measurement combined with a neural-network analysis is useful for plastic recycling.

  20. 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

  1. Neural networks for triggering

    SciTech Connect

    Denby, B. ); Campbell, M. ); Bedeschi, F. ); Chriss, N.; Bowers, C. ); Nesti, F. )

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab.

  2. High-performance neural networks. [Neural computers

    SciTech Connect

    Dress, W.B.

    1987-06-01

    The new Forth hardware architectures offer an intermediate solution to high-performance neural networks while the theory and programming details of neural networks for synthetic intelligence are developed. This approach has been used successfully to determine the parameters and run the resulting network for a synthetic insect consisting of a 200-node ''brain'' with 1760 interconnections. Both the insect's environment and its sensor input have thus far been simulated. However, the frequency-coded nature of the Browning network allows easy replacement of the simulated sensors by real-world counterparts.

  3. Program Helps Simulate Neural Networks

    NASA Technical Reports Server (NTRS)

    Villarreal, James; Mcintire, Gary

    1993-01-01

    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  4. Stimulated Photorefractive Optical Neural Networks

    DTIC Science & Technology

    1992-12-15

    This final report describes research in optical neural networks performed under DARPA sponsorship at Hughes Aircraft Company during the period 1989...in photorefractive crystals. This approach reduces crosstalk and improves the utilization of the optical input device. Successfully implemented neural ... networks include the Perceptron, Bidirectional Associative Memory, and multi-layer backpropagation networks. Up to 104 neurons, 2xl0(7) weights, and

  5. Analysis of Simple Neural Networks

    DTIC Science & Technology

    1988-12-20

    ANALYSIS OF SThlPLE NEURAL NETWORKS Chedsada Chinrungrueng Master’s Report Under the Supervision of Prof. Carlo H. Sequin Department of... Neural Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT...and guidJ.nce. I have learned a great deal from his teaching, knowledge, and criti- cism. 1. MOTIVATION ANALYSIS OF SIMPLE NEURAL NETWORKS Chedsada

  6. Neural Networks For Robot Control

    DTIC Science & Technology

    2001-04-17

    following: (a) Application of artificial neural networks (multi-layer perceptrons, MLPs) for 2D planar robot arm by using the dynamic backpropagation...methods for the adjustment of parameters; and optimization of the architecture; (b) Application of artificial neural networks in controlling closed...studies in controlling dynamic robot arms by using neural networks in real-time process; (2) Research of optimal architectures used in closed-loop systems in order to compare with adaptive and robust control.

  7. 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

  8. Optical-Correlator Neural Network Based On Neocognitron

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

    Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

  9. Neural Networks, Reliability and Data Analysis

    DTIC Science & Technology

    1993-01-01

    Neural network technology has been surveyed with the intent of determining the feasibility and impact neural networks may have in the area of...automated reliability tools. Data analysis capabilities of neural networks appear to be very applicable to reliability science due to similar mathematical...tendencies in data.... Neural networks , Reliability, Data analysis, Automated reliability tools, Automated intelligent information processing, Statistical neural network.

  10. [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.

  11. Accelerating Learning By Neural Networks

    NASA Technical Reports Server (NTRS)

    Toomarian, Nikzad; Barhen, Jacob

    1992-01-01

    Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

  12. Interacting neural networks

    NASA Astrophysics Data System (ADS)

    Metzler, R.; Kinzel, W.; Kanter, I.

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

  13. Dynamic interactions in neural networks

    SciTech Connect

    Arbib, M.A. ); Amari, S. )

    1989-01-01

    The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of intelligent machines. This volume presents models and data on the dynamic interactions occurring in the brain, and exhibits the dynamic interactions between research in computational neuroscience and in neural computing. The authors present current research, future trends and open problems.

  14. Technology Assessment of Neural Networks

    DTIC Science & Technology

    1989-02-13

    Unlike a Von Neumann type of computer which needs to be programmed to carry out an information-processing function, neural networks are promised as...trainable through a series of trials to learn how to process information. An assessment of the current, near-term (1995), and long-term (2010) trends in Neural Networks is given.

  15. Phase Detection Using Neural Networks.

    DTIC Science & Technology

    1997-03-10

    A likelihood of detecting a reflected signal characterized by phase discontinuities and background noise is enhanced by utilizing neural networks to...identify coherency intervals. The received signal is processed into a predetermined format such as a digital time series. Neural networks perform

  16. Neural network applications in telecommunications

    NASA Technical Reports Server (NTRS)

    Alspector, Joshua

    1994-01-01

    Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

  17. Neural Networks for the Beginner.

    ERIC Educational Resources Information Center

    Snyder, Robin M.

    Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

  18. Hybrid Neural Network for Pattern Recognition.

    DTIC Science & Technology

    1997-02-03

    two one-layer neural networks and the second stage comprises a feedforward two-layer neural network . A method for recognizing patterns is also...topological representations of the input patterns using the first and second neural networks. The method further comprises providing a third neural network for...classifying and recognizing the inputted patterns and training the third neural network with a back-propagation algorithm so that the third neural network recognizes at least one interested pattern.

  19. Bulbar acetylcholine enhances neural and perceptual odor discrimination.

    PubMed

    Chaudhury, Dipesh; Escanilla, Olga; Linster, Christiane

    2009-01-07

    Experimental and modeling data suggest that the circuitry of the main olfactory bulb (OB) plays a critical role in olfactory discrimination. Processing of such information arises from the interaction between OB output neurons local interneurons, as well as interactions between the OB network and centrifugal inputs. Cholinergic input to the OB in particular has been hypothesized to regulate mitral cell odorants receptive fields (ORFs) and behavioral discrimination of similar odorants. We recorded from individual mitral cells in the OB in anesthetized rats to determine the degree of overlap in ORFs of individual mitral cells after exposure to odorant stimuli. Increasing the efficacy of the cholinergic neurotransmission in the OB by addition of the anticholinesterase drug neostigmine (20 mM) sharpened the ORF responses of mitral cells. Furthermore, coaddition of either the nicotinic antagonist methyllycaconitine citrate hydrate (MLA) (20 mM) or muscarinic antagonist scopolamine (40 mM) together with neostigmine (20 mM) attenuated the neostigmine-dependent sharpening of ORFs. These electrophysiological findings are predictive of accompanying behavioral experiments in which cholinergic modulation was manipulated by direct infusion of neostigmine, MLA, and scopolamine into the OB during olfactory behavioral tasks. Increasing the efficacy of cholinergic action in the OB increased perceptual discrimination of odorants in these experiments, whereas blockade of nicotinic or muscarinic receptors decreased perceptual discrimination. These experiments show that behavioral discrimination is modulated in a manner predicted by the changes in mitral cell ORFs by cholinergic drugs. These results together present a first direct comparison between neural and perceptual effects of a bulbar neuromodulator.

  20. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

    PubMed Central

    2011-01-01

    Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed

  1. 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.

  2. Neural networks in astronomy.

    PubMed

    Tagliaferri, Roberto; Longo, Giuseppe; Milano, Leopoldo; Acernese, Fausto; Barone, Fabrizio; Ciaramella, Angelo; De Rosa, Rosario; Donalek, Ciro; Eleuteri, Antonio; Raiconi, Giancarlo; Sessa, Salvatore; Staiano, Antonino; Volpicelli, Alfredo

    2003-01-01

    In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).

  3. Neural networks for calibration tomography

    NASA Technical Reports Server (NTRS)

    Decker, Arthur

    1993-01-01

    Artificial neural networks are suitable for performing pattern-to-pattern calibrations. These calibrations are potentially useful for facilities operations in aeronautics, the control of optical alignment, and the like. Computed tomography is compared with neural net calibration tomography for estimating density from its x-ray transform. X-ray transforms are measured, for example, in diffuse-illumination, holographic interferometry of fluids. Computed tomography and neural net calibration tomography are shown to have comparable performance for a 10 degree viewing cone and 29 interferograms within that cone. The system of tomography discussed is proposed as a relevant test of neural networks and other parallel processors intended for using flow visualization data.

  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. Neural Networks for Readability Analysis.

    ERIC Educational Resources Information Center

    McEneaney, John E.

    This paper describes and reports on the performance of six related artificial neural networks that have been developed for the purpose of readability analysis. Two networks employ counts of linguistic variables that simulate a traditional regression-based approach to readability. The remaining networks determine readability from "visual…

  6. A Complexity Theory of Neural Networks

    DTIC Science & Technology

    1990-04-14

    Significant results have been obtained on the computation complexity of analog neural networks , and distribute voting. The computing power and...learning algorithms for limited precision analog neural networks have been investigated. Lower bounds for constant depth, polynomial size analog neural ... networks , and a limited version of discrete neural networks have been obtained. The work on distributed voting has important applications for distributed

  7. Collective Computation of Neural Network

    DTIC Science & Technology

    1990-03-15

    Sciences, Beijing ABSTRACT Computational neuroscience is a new branch of neuroscience originating from current research on the theory of computer...scientists working in artificial intelligence engineering and neuroscience . The paper introduces the collective computational properties of model neural...vision research. On this basis, the authors analyzed the significance of the Hopfield model. Key phrases: Computational Neuroscience , Neural Network, Model

  8. Localizing Tortoise Nests by Neural Networks.

    PubMed

    Barbuti, Roberto; Chessa, Stefano; Micheli, Alessio; Pucci, Rita

    2016-01-01

    The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.

  9. Localizing Tortoise Nests by Neural Networks

    PubMed Central

    2016-01-01

    The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition. PMID:26985660

  10. 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.

  11. The holographic neural network: Performance comparison with other neural networks

    NASA Astrophysics Data System (ADS)

    Klepko, Robert

    1991-10-01

    The artificial neural network shows promise for use in recognition of high resolution radar images of ships. The holographic neural network (HNN) promises a very large data storage capacity and excellent generalization capability, both of which can be achieved with only a few learning trials, unlike most neural networks which require on the order of thousands of learning trials. The HNN is specially designed for pattern association storage, and mathematically realizes the storage and retrieval mechanisms of holograms. The pattern recognition capability of the HNN was studied, and its performance was compared with five other commonly used neural networks: the Adaline, Hamming, bidirectional associative memory, recirculation, and back propagation networks. The patterns used for testing represented artificial high resolution radar images of ships, and appear as a two dimensional topology of peaks with various amplitudes. The performance comparisons showed that the HNN does not perform as well as the other neural networks when using the same test data. However, modification of the data to make it appear more Gaussian distributed, improved the performance of the network. The HNN performs best if the data is completely Gaussian distributed.

  12. Signature neural networks: definition and application to multidimensional sorting problems.

    PubMed

    Latorre, Roberto; de Borja Rodriguez, Francisco; Varona, Pablo

    2011-01-01

    In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: 1) neural signatures to identify each unit in the network; 2) local discrimination of input information during the processing; and 3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

  13. Neural networks predict tomato maturity stage

    NASA Astrophysics Data System (ADS)

    Hahn, Federico

    1999-03-01

    Almost 40% of the total horticultural produce exported from Mexico the USA is tomato, and quality is fundamental for maintaining the market. Many fruits packed at the green-mature stage do not mature towards a red color as they were harvested before achieving its physiological maturity. Tomato gassed for advancing maturation does not respond on those fruits, and repacking is necessary at terminal markets, causing losses to the producer. Tomato spectral signatures are different on each maturity stage and tomato size was poorly correlated against peak wavelengths. A back-propagation neural network was used to predict tomato maturity using reflectance ratios as inputs. Higher success rates were achieved on tomato maturity stage recognition with neural networks than with discriminant analysis.

  14. VLSI implementation of neural networks.

    PubMed

    Wilamowski, B M; Binfet, J; Kaynak, M O

    2000-06-01

    Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries. Two neural networks were designed in 1.5 microm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.

  15. Numerical discrimination is mediated by neural coding variation.

    PubMed

    Prather, Richard W

    2014-12-01

    One foundation of numerical cognition is that discrimination accuracy depends on the proportional difference between compared values, closely following the Weber-Fechner discrimination law. Performance in non-symbolic numerical discrimination is used to calculate individual Weber fraction, a measure of relative acuity of the approximate number system (ANS). Individual Weber fraction is linked to symbolic arithmetic skills and long-term educational and economic outcomes. The present findings suggest that numerical discrimination performance depends on both the proportional difference and absolute value, deviating from the Weber-Fechner law. The effect of absolute value is predicted via computational model based on the neural correlates of numerical perception. Specifically, that the neural coding "noise" varies across corresponding numerosities. A computational model using firing rate variation based on neural data demonstrates a significant interaction between ratio difference and absolute value in predicting numerical discriminability. We find that both behavioral and computational data show an interaction between ratio difference and absolute value on numerical discrimination accuracy. These results further suggest a reexamination of the mechanisms involved in non-symbolic numerical discrimination, how researchers may measure individual performance, and what outcomes performance may predict.

  16. A joint compression-discrimination neural transformation applied to target detection.

    PubMed

    Chan, Alex Lipchen; Der, Sandor Z; Nasrabadi, Nasser M

    2005-08-01

    Many image recognition algorithms based on data-learning perform dimensionality reduction before the actual learning and classification because the high dimensionality of raw imagery would require enormous training sets to achieve satisfactory performance. A potential problem with this approach is that most dimensionality reduction techniques, such as principal component analysis (PCA), seek to maximize the representation of data variation into a small number of PCA components, without considering interclass discriminability. This paper presents a neural-network-based transformation that simultaneously seeks to provide dimensionality reduction and a high degree of discriminability by combining together the learning mechanism of a neural-network-based PCA and a backpropagation learning algorithm. The joint discrimination-compression algorithm is applied to infrared imagery to detect military vehicles.

  17. Antenna analysis using neural networks

    NASA Technical Reports Server (NTRS)

    Smith, William T.

    1992-01-01

    Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

  18. Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

    PubMed

    Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

    2016-07-14

    This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

  19. Categorization in neural networks and prosopagnosia

    NASA Astrophysics Data System (ADS)

    Virasoro, M. A.

    1989-12-01

    Prosopagnosia is a syndrome characterized by a generalized difficulty to visually recognize individual patterns among those that are similar, and can therefore be said to belong to the same category. I suggest that the existence of this disfunction may be an important clue for understanding the categorization process in the brain. In this direction the performance of neural networks under random destruction of synapses is analysed. It is found that in almost every network that stores correlated patterns the coding of the discriminating details between individuals inside a class is more sensitive to noise or to random destruction than the coding that distinguishes between classes. It follows that a process of death and/or deterioration at an intermediate level of intensity, even if it acts randomly on the network may lead to a malfunctioning of the network that resembles prosopagnosia.

  20. Binaural Sound Localization Using Neural Networks

    DTIC Science & Technology

    1991-12-12

    by Brennan, involved the implementation of a neural network to model the ability of a bat to discriminate between a mealworm and an inedible object...locate, identify and capture airborne prey (6:2). The sonar returns were collected from the mealworms , spheres and disks at various rotations (90 to...order to meet the criteria. If 75 out of 100 test vectors met the criteria, then P(HHN) = 0.75. P(FBE I E): The probability that a classification error

  1. Applying neural networks to ultrasonographic texture recognition

    NASA Astrophysics Data System (ADS)

    Gallant, Jean-Francois; Meunier, Jean; Stampfler, Robert; Cloutier, Jocelyn

    1993-09-01

    A neural network was trained to classify ultrasound image samples of normal, adenomatous (benign tumor) and carcinomatous (malignant tumor) thyroid gland tissue. The samples themselves, as well as their Fourier spectrum, miscellaneous cooccurrence matrices and 'generalized' cooccurrence matrices, were successively submitted to the network, to determine if it could be trained to identify discriminating features of the texture of the image, and if not, which feature extractor would give the best results. Results indicate that the network could indeed extract some distinctive features from the textures, since it could accomplish a partial classification when trained with the samples themselves. But a significant improvement both in learning speed and performance was observed when it was trained with the generalized cooccurrence matrices of the samples.

  2. Signal Approximation with a Wavelet Neural Network

    DTIC Science & Technology

    1992-12-01

    specialized electronic devices like the Intel Electronically Trainable Analog Neural Network (ETANN) chip. The WNN representation allows the...accurately approximated with a WNN trained with irregularly sampled data. Signal approximation, Wavelet neural network .

  3. A Neural Network Based Speech Recognition System

    DTIC Science & Technology

    1990-02-01

    encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection...environment. Keywords: Artificial intelligence; Neural networks : Back propagation; Speech recognition.

  4. 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.

  5. Neural correlates of abnormal sensory discrimination in laryngeal dystonia.

    PubMed

    Termsarasab, Pichet; Ramdhani, Ritesh A; Battistella, Giovanni; Rubien-Thomas, Estee; Choy, Melissa; Farwell, Ian M; Velickovic, Miodrag; Blitzer, Andrew; Frucht, Steven J; Reilly, Richard B; Hutchinson, Michael; Ozelius, Laurie J; Simonyan, Kristina

    2016-01-01

    Aberrant sensory processing plays a fundamental role in the pathophysiology of dystonia; however, its underpinning neural mechanisms in relation to dystonia phenotype and genotype remain unclear. We examined temporal and spatial discrimination thresholds in patients with isolated laryngeal form of dystonia (LD), who exhibited different clinical phenotypes (adductor vs. abductor forms) and potentially different genotypes (sporadic vs. familial forms). We correlated our behavioral findings with the brain gray matter volume and functional activity during resting and symptomatic speech production. We found that temporal but not spatial discrimination was significantly altered across all forms of LD, with higher frequency of abnormalities seen in familial than sporadic patients. Common neural correlates of abnormal temporal discrimination across all forms were found with structural and functional changes in the middle frontal and primary somatosensory cortices. In addition, patients with familial LD had greater cerebellar involvement in processing of altered temporal discrimination, whereas sporadic LD patients had greater recruitment of the putamen and sensorimotor cortex. Based on the clinical phenotype, adductor form-specific correlations between abnormal discrimination and brain changes were found in the frontal cortex, whereas abductor form-specific correlations were observed in the cerebellum and putamen. Our behavioral and neuroimaging findings outline the relationship of abnormal sensory discrimination with the phenotype and genotype of isolated LD, suggesting the presence of potentially divergent pathophysiological pathways underlying different manifestations of this disorder.

  6. Neural Networks for Flight Control

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1996-01-01

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

  7. Analysis and Design of Neural Networks

    DTIC Science & Technology

    1992-01-01

    The training problem for feedforward neural networks is nonlinear parameter estimation that can be solved by a variety of optimization techniques...Much of the literature of neural networks has focused on variants of gradient descent. The training of neural networks using such techniques is known to...be a slow process with more sophisticated techniques not always performing significantly better. It is shown that feedforward neural networks can

  8. Radar System Classification Using Neural Networks

    DTIC Science & Technology

    1991-12-01

    This study investigated methods of improving the accuracy of neural networks in the classification of large numbers of classes. A literature search...revealed that neural networks have been successful in the radar classification problem, and that many complex problems have been solved using systems...of multiple neural networks . The experiments conducted were based on 32 classes of radar system data. The neural networks were modelled using a program

  9. Neural networks and applications tutorial

    NASA Astrophysics Data System (ADS)

    Guyon, I.

    1991-09-01

    The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

  10. Fault Tolerance of Neural Networks

    DTIC Science & Technology

    1994-07-01

    Systematic Ap - proach, Proc. Government Microcircuit Application Conf. (GOMAC), San Diego, Nov. 1986. [10] D.E.Goldberg, Genetic Algorithms in Search...s l m n ttempt to develop fault tolerant neural networks. The lows. Given a well-trained network, we first eliminate temp todevlopfaut tlernt eurl ...both ap - proaches, and this resulted in very slight improve- ments over the addition/deletion procedure. 103 Fisher’s Iris data in average case Fisher’s

  11. Neural network-based retrieval from software reuse repositories

    NASA Technical Reports Server (NTRS)

    Eichmann, David A.; Srinivas, Kankanahalli

    1992-01-01

    A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline an approach to this problem based upon neural networks which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary.

  12. 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.

  13. 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

  14. Model Of Neural Network With Creative Dynamics

    NASA Technical Reports Server (NTRS)

    Zak, Michail; Barhen, Jacob

    1993-01-01

    Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.

  15. How Neural Networks Learn from Experience.

    ERIC Educational Resources Information Center

    Hinton, Geoffrey E.

    1992-01-01

    Discusses computational studies of learning in artificial neural networks and findings that may provide insights into the learning abilities of the human brain. Describes efforts to test theories about brain information processing, using artificial neural networks. Vignettes include information concerning how a neural network represents…

  16. The neural mechanism of binocular depth discrimination

    PubMed Central

    Barlow, H. B.; Blakemore, C.; Pettigrew, J. D.

    1967-01-01

    1. Binocularly driven units were investigated in the cat's primary visual cortex. 2. It was found that a stimulus located correctly in the visual fields of both eyes was more effective in driving the units than a monocular stimulus, and much more effective than a binocular stimulus which was correctly positioned in only one eye: the response to the correctly located image in one eye is vetoed if the image is incorrectly located in the other eye. 3. The vertical and horizontal disparities of the paired retinal images that yielded the maximum response were measured in 87 units from seven cats: the range of horizontal disparities was 6·6°, of vertical disparities 2·2°. 4. With fixed convergence, different units will be optimally excited by objects lying at different distances. This may be the basic mechanism underlying depth discrimination in the cat. PMID:6065881

  17. Discrimination of crop and weeds on visible and visible/near-infrared spectrums using support vector machines, artificial neural network and decision tree

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Weeds are regarded as farmers' natural enemy. In order to avoid excessive pesticide residues, the destruction of ecological environment, and to guarantee the quality and safety of agricultural products, it is urgent to develop highly-efficient weed management methods. Amongst, weed discrimination is...

  18. Neural networks for atmospheric retrievals

    NASA Technical Reports Server (NTRS)

    Motteler, Howard E.; Gualtieri, J. A.; Strow, L. Larrabee; Mcmillin, Larry

    1993-01-01

    We use neural networks to perform retrievals of temperature and water fractions from simulated clear air radiances for the Atmospheric Infrared Sounder (AIRS). Neural networks allow us to make effective use of the large AIRS channel set, and give good performance with noisy input. We retrieve surface temperature, air temperature at 64 distinct pressure levels, and water fractions at 50 distinct pressure levels. Using 728 temperature and surface sensitive channels, the RMS error for temperature retrievals with 0.2K input noise is 1.2K. Using 586 water and temperature sensitive channels, the mean error with 0.2K input noise is 16 percent. Our implementation of backpropagation training for neural networks on the 16,000-processor MasPar MP-1 runs at a rate of 90 million weight updates per second, and allows us to train large networks in a reasonable amount of time. Once trained, the network can be used to perform retrievals quickly on a workstation of moderate power.

  19. Gamma oscillations of spiking neural populations enhance signal discrimination.

    PubMed

    Masuda, Naoki; Doiron, Brent

    2007-11-01

    Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive.

  20. Training Neural Networks with Weight Constraints

    DTIC Science & Technology

    1993-03-01

    Hardware implementation of artificial neural networks imposes a variety of constraints. Finite weight magnitudes exist in both digital and analog...optimizing a network with weight constraints. Comparisons are made to the backpropagation training algorithm for networks with both unconstrained and hard-limited weight magnitudes. Neural networks , Analog, Digital, Stochastic

  1. Correlated neural variability in persistent state networks.

    PubMed

    Polk, Amber; Litwin-Kumar, Ashok; Doiron, Brent

    2012-04-17

    Neural activity that persists long after stimulus presentation is a biological correlate of short-term memory. Variability in spiking activity causes persistent states to drift over time, ultimately degrading memory. Models of short-term memory often assume that the input fluctuations to neural populations are independent across cells, a feature that attenuates population-level variability and stabilizes persistent activity. However, this assumption is at odds with experimental recordings from pairs of cortical neurons showing that both the input currents and output spike trains are correlated. It remains unclear how correlated variability affects the stability of persistent activity and the performance of cognitive tasks that it supports. We consider the stochastic long-timescale attractor dynamics of pairs of mutually inhibitory populations of spiking neurons. In these networks, persistent activity was less variable when correlated variability was globally distributed across both populations compared with the case when correlations were locally distributed only within each population. Using a reduced firing rate model with a continuum of persistent states, we show that, when input fluctuations are correlated across both populations, they drive firing rate fluctuations orthogonal to the persistent state attractor, thereby causing minimal stochastic drift. Using these insights, we establish that distributing correlated fluctuations globally as opposed to locally improves network's performance on a two-interval, delayed response discrimination task. Our work shows that the correlation structure of input fluctuations to a network is an important factor when determining long-timescale, persistent population spiking activity.

  2. An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks

    DTIC Science & Technology

    2002-03-01

    The research contribution of this thesis is the first known integrated architecture and feature selection algorithm for Radial Basis Neural Networks (RBNNs...Additionally, this thesis compares three different classification techniques, Discriminant Analysis (DA), Feed-Forward Neural Networks (FFN) and RBNNs against

  3. 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)

  4. Terminal attractors in neural networks

    NASA Technical Reports Server (NTRS)

    Zak, Michail

    1989-01-01

    A new type of attractor (terminal attractors) for content-addressable memory, associative memory, and pattern recognition in artificial neural networks operating in continuous time is introduced. The idea of a terminal attractor is based upon a violation of the Lipschitz condition at a fixed point. As a result, the fixed point becomes a singular solution which envelopes the family of regular solutions, while each regular solution approaches such an attractor in finite time. It will be shown that terminal attractors can be incorporated into neural networks such that any desired set of these attractors with prescribed basins is provided by an appropriate selection of the synaptic weights. The applications of terminal attractors for content-addressable and associative memories, pattern recognition, self-organization, and for dynamical training are illustrated.

  5. Fiber optic Adaline neural networks

    NASA Astrophysics Data System (ADS)

    Ghosh, Anjan K.; Trepka, Jim; Paparao, Palacharla

    1993-02-01

    Optoelectronic realization of adaptive filters and equalizers using fiber optic tapped delay lines and spatial light modulators has been discussed recently. We describe the design of a single layer fiber optic Adaline neural network which can be used as a bit pattern classifier. In our realization we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in processing speed, parallelism, and interconnection. The new optical neural network described in this paper is designed for optical processing of guided lightwave signals, not electronic signals. We analyzed the convergence or learning characteristics of the optically implemented Adaline in the presence of errors in the hardware, and we studied methods for improving the convergence rate of the Adaline.

  6. Prototype neural network pattern recognition testbed

    NASA Astrophysics Data System (ADS)

    Worrell, Steven W.; Robertson, James A.; Varner, Thomas L.; Garvin, Charles G.

    1991-02-01

    Recent successes ofneural networks has led to an optimistic outlook for neural network applications to image processing(IP). This paperpresents a general architecture for performing comparative studies of neural processing and more conventional IF techniques as well as hybrid pattern recognition (PR) systems. Two hybrid PR systems have been simulated each of which incorporate both conventional and neural processing techniques.

  7. Neural Network for Visual Search Classification

    DTIC Science & Technology

    2007-11-02

    neural network used to perform visual search classification. The neural network consists of a Learning vector quantization network (LVQ) and a single layer perceptron. The objective of this neural network is to classify the various human visual search patterns into predetermined classes. The classes signify the different search strategies used by individuals to scan the same target pattern. The input search patterns are quantified with respect to an ideal search pattern, determined by the user. A supervised learning rule,

  8. The LILARTI neural network system

    SciTech Connect

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  9. Characterization of Early Cortical Neural Network ...

    EPA Pesticide Factsheets

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentially absent on DIV 2 and developed rapidly between DIV 5 and 12. Spiking activity was primarily sporadic and unorganized at early DIV, and became progressively more organized with time in culture, with bursting parameters, synchrony and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity and principal components analysis using these features demonstrated a general segregation of data by age at both the well and plate levels. Using a combination of random forest classifiers and Support Vector Machines, we demonstrated that 4 features (CV of within burst ISI, CV of IBI, network spike rate and burst rate) were sufficient to predict the age (either DIV 5, 7, 9 or 12) of each well recording with >65% accuracy. When restricting the classification problem to a binary decision, we found that classification improved dramatically, e.g. 95% accuracy for discriminating DIV 5 vs DIV 12 wells. Further, we present a novel resampling approach to determine the number of wells that might be needed for conducting comparisons of different treatments using mwMEA plates. Overall, these results demonstrate that network development on mwMEA plates is similar to

  10. Neural network modeling of emotion

    NASA Astrophysics Data System (ADS)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  11. Feature Extraction Using an Unsupervised Neural Network

    DTIC Science & Technology

    1991-05-03

    A novel unsupervised neural network for dimensionality reduction which seeks directions emphasizing distinguishing features in the data is presented. A statistical framework for the parameter estimation problem associated with this neural network is given and its connection to exploratory projection pursuit methods is established. The network is shown to minimize a loss function (projection index) over a

  12. Neural networks and MIMD-multiprocessors

    NASA Technical Reports Server (NTRS)

    Vanhala, Jukka; Kaski, Kimmo

    1990-01-01

    Two artificial neural network models are compared. They are the Hopfield Neural Network Model and the Sparse Distributed Memory model. Distributed algorithms for both of them are designed and implemented. The run time characteristics of the algorithms are analyzed theoretically and tested in practice. The storage capacities of the networks are compared. Implementations are done using a distributed multiprocessor system.

  13. Neural-Network Computer Transforms Coordinates

    NASA Technical Reports Server (NTRS)

    Josin, Gary M.

    1990-01-01

    Numerical simulation demonstrated ability of conceptual neural-network computer to generalize what it has "learned" from few examples. Ability to generalize achieved with even simple neural network (relatively few neurons) and after exposure of network to only few "training" examples. Ability to obtain fairly accurate mappings after only few training examples used to provide solutions to otherwise intractable mapping problems.

  14. Satellite image analysis using neural networks

    NASA Technical Reports Server (NTRS)

    Sheldon, Roger A.

    1990-01-01

    The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

  15. Oil reservoir properties estimation using neural networks

    SciTech Connect

    Toomarian, N.B.; Barhen, J.; Glover, C.W.; Aminzadeh, F.

    1997-02-01

    This paper investigates the applicability as well as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data. This approach relies on JPL`s adjoint operators general purpose neural network code to determine the best suited architecture. The authors believe that results presented in this work demonstrate that artificial neural networks produce surprisingly accurate estimates of the reservoir parameters.

  16. Adaptive optimization and control using neural networks

    SciTech Connect

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

    1993-10-22

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

  17. Neural Network Retinal Model Real Time Implementation

    DTIC Science & Technology

    1992-09-02

    addresses the specific needs of vision processing. The goal of this SBIR Phase I project has been to take a significant neural network vision...application and to map it onto dedicated hardware for real time implementation. The neural network was already demonstrated using software simulation on a...general purpose computer. During Phase 1, HNC took a neural network model of the retina and, using HNC’s Vision Processor (ViP) prototype hardware

  18. Neural Network False Alarm Filter. Volume 1.

    DTIC Science & Technology

    1994-12-01

    This effort identified, developed and demonstrated a set of approaches for applying neural network learning techniques to the development of a real... neural network models, 9 fault report causes and 12 common groups of BIT techniques was identified. From this space, 4 unique, high-potential...of their strengths and weaknesses were performed along with cost/ benefit analyses. This study concluded that the best candidates for neural network insert

  19. A Neural Network Object Recognition System

    DTIC Science & Technology

    1990-07-01

    useful for exploring different neural network configurations. There are three main computation phases of a model based object recognition system...segmentation, feature extraction, and object classification. This report focuses on the object classification stage. For segmentation, a neural network based...are available with the current system. Neural network based feature extraction may be added at a later date. The classification stage consists of a

  20. Neural Networks Applied to Signal Processing

    DTIC Science & Technology

    1989-09-01

    DTIC FILE COpy NAVAL POSTGRADUATE SCHOOL . Monterey, California Lf 0 (0 V’ STATES 4 THESIS NEURAL NETWORKS APPLIED TO SIGNAL PROCESSING by Mark D...FUNDING NUMBERS PROGRAM PROJECT TASK WORK UNIT ELEMENT NO NO NO ACCESSION NO. 11. TITLE (Include Security Classification) NEURAL NETWORKS APPLIED TO...for public release; distribution is unlimited Neural Networks Applied to Signal Processing by Mark D. Baehre Captain, United States Army B.S., United

  1. A Complexity Theory of Neural Networks

    DTIC Science & Technology

    1991-08-09

    Significant progress has been made in laying the foundations of a complexity theory of neural networks . The fundamental complexity classes have been...identified and studied. The class of problems solvable by small, shallow neural networks has been found to be the same class even if (1) probabilistic...behaviour (2)Multi-valued logic, and (3)analog behaviour, are allowed (subject to certain resonable technical assumptions). Neural networks can be

  2. Neural network architecture for crossbar switch control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry P.; Walters, Stephen M.

    1991-01-01

    A Hopfield neural network architecture for the real-time control of a crossbar switch for switching packets at maximum throughput is proposed. The network performance and processing time are derived from a numerical simulation of the transitions of the neural network. A method is proposed to optimize electronic component parameters and synaptic connections, and it is fully illustrated by the computer simulation of a VLSI implementation of 4 x 4 neural net controller. The extension to larger size crossbars is demonstrated through the simulation of an 8 x 8 crossbar switch controller, where the performance of the neural computation is discussed in relation to electronic noise and inhomogeneities of network components.

  3. Neural network based system for equipment surveillance

    DOEpatents

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  4. Neural network based system for equipment surveillance

    DOEpatents

    Vilim, Richard B.; Gross, Kenneth C.; Wegerich, Stephan W.

    1998-01-01

    A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

  5. Advances in neural networks research: an introduction.

    PubMed

    Kozma, Robert; Bressler, Steven; Perlovsky, Leonid; Venayagamoorthy, Ganesh Kumar

    2009-01-01

    The present Special Issue "Advances in Neural Networks Research: IJCNN2009" provides a state-of-art overview of the field of neural networks. It includes 39 papers from selected areas of the 2009 International Joint Conference on Neural Networks (IJCNN2009). IJCNN2009 took place on June 14-19, 2009 in Atlanta, Georgia, USA, and it represents an exemplary collaboration between the International Neural Networks Society and the IEEE Computational Intelligence Society. Topics in this issue include neuroscience and cognitive science, computational intelligence and machine learning, hybrid techniques, nonlinear dynamics and chaos, various soft computing technologies, intelligent signal processing and pattern recognition, bioinformatics and biomedicine, and engineering applications.

  6. Electronic neural networks for global optimization

    NASA Technical Reports Server (NTRS)

    Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.

    1990-01-01

    An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.

  7. Neural encoding of auditory discrimination in ventral premotor cortex

    PubMed Central

    Lemus, Luis; Hernández, Adrián; Romo, Ranulfo

    2009-01-01

    Monkeys have the capacity to accurately discriminate the difference between two acoustic flutter stimuli. In this task, monkeys must compare information about the second stimulus to the memory trace of the first stimulus, and must postpone the decision report until a sensory cue triggers the beginning of the decision motor report. The neuronal processes associated with the different components of this task have been investigated in the primary auditory cortex (A1); but, A1 seems exclusively associated with the sensory and not with the working memory and decision components of this task. Here, we show that ventral premotor cortex (VPC) neurons reflect in their activities the current and remembered acoustic stimulus, their comparison, and the result of the animal's decision report. These results provide evidence that the neural dynamics of VPC is involved in the processing steps that link sensation and decision-making during auditory discrimination. PMID:19667191

  8. Neural Networks for Rapid Design and Analysis

    NASA Technical Reports Server (NTRS)

    Sparks, Dean W., Jr.; Maghami, Peiman G.

    1998-01-01

    Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

  9. Aerodynamic Design Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan; Madavan, Nateri K.

    2003-01-01

    The design of aerodynamic components of aircraft, such as wings or engines, involves a process of obtaining the most optimal component shape that can deliver the desired level of component performance, subject to various constraints, e.g., total weight or cost, that the component must satisfy. Aerodynamic design can thus be formulated as an optimization problem that involves the minimization of an objective function subject to constraints. A new aerodynamic design optimization procedure based on neural networks and response surface methodology (RSM) incorporates the advantages of both traditional RSM and neural networks. The procedure uses a strategy, denoted parameter-based partitioning of the design space, to construct a sequence of response surfaces based on both neural networks and polynomial fits to traverse the design space in search of the optimal solution. Some desirable characteristics of the new design optimization procedure include the ability to handle a variety of design objectives, easily impose constraints, and incorporate design guidelines and rules of thumb. It provides an infrastructure for variable fidelity analysis and reduces the cost of computation by using less-expensive, lower fidelity simulations in the early stages of the design evolution. The initial or starting design can be far from optimal. The procedure is easy and economical to use in large-dimensional design space and can be used to perform design tradeoff studies rapidly. Designs involving multiple disciplines can also be optimized. Some practical applications of the design procedure that have demonstrated some of its capabilities include the inverse design of an optimal turbine airfoil starting from a generic shape and the redesign of transonic turbines to improve their unsteady aerodynamic characteristics.

  10. Neural networks for nuclear spectroscopy

    SciTech Connect

    Keller, P.E.; Kangas, L.J.; Hashem, S.; Kouzes, R.T.

    1995-12-31

    In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN. In this system the ANN is used to automatically identify, radioactive isotopes in real-time from their gamma-ray spectra. Two neural network paradigms are examined: the linear perception and the optimal linear associative memory (OLAM). A comparison of the two paradigms shows that OLAM is superior to linear perception for this application. Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the whole spectrum in the identification process instead of only the individual photo-peaks. For this reason, it is potentially more useful for processing data from lower resolution gamma-ray spectrometers. This approach has been tested with data generated by Monte Carlo simulations and with field data from sodium iodide and Germanium detectors. With the ANN approach, the intense computation takes place during the training process. Once the network is trained, normal operation consists of propagating the data through the network, which results in rapid identification of samples. This approach is useful in situations that require fast response where precise quantification is less important.

  11. Flow Control Using Neural Networks

    DTIC Science & Technology

    2007-11-02

    FEB 93 - 31 DEC 96 4. TITLE AND SUBTITLE 5 . FUNDING NUMBERS FLOW CONTROL USING NEURAL NETWORKS F49620-93-1-0135 61102F 6. AUTHOR(S) 2307/BS THORWALD...OFFICE OF SCIENTIFIC RESEARCH (AFOSRO AGENCY REPORT NUMBER 110 DUNCAN AVENUE, ROOM B115 BOLLING AFB DC 20332- 8050 11. SUPPLEMENTARY NOTES 12a...signals. Figure 5 shows a time series for an actuator that performs a ramp motion in the streamwise direction over about 1 % of the TS period and remains

  12. Neural Network Classifies Teleoperation Data

    NASA Technical Reports Server (NTRS)

    Fiorini, Paolo; Giancaspro, Antonio; Losito, Sergio; Pasquariello, Guido

    1994-01-01

    Prototype artificial neural network, implemented in software, identifies phases of telemanipulator tasks in real time by analyzing feedback signals from force sensors on manipulator hand. Prototype is early, subsystem-level product of continuing effort to develop automated system that assists in training and supervising human control operator: provides symbolic feedback (e.g., warnings of impending collisions or evaluations of performance) to operator in real time during successive executions of same task. Also simplifies transition between teleoperation and autonomous modes of telerobotic system.

  13. The Laplacian spectrum of neural networks.

    PubMed

    de Lange, Siemon C; de Reus, Marcel A; van den Heuvel, Martijn P

    2014-01-13

    The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these "conventional" graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks.

  14. Three dimensional living neural networks

    NASA Astrophysics Data System (ADS)

    Linnenberger, Anna; McLeod, Robert R.; Basta, Tamara; Stowell, Michael H. B.

    2015-08-01

    We investigate holographic optical tweezing combined with step-and-repeat maskless projection micro-stereolithography for fine control of 3D positioning of living cells within a 3D microstructured hydrogel grid. Samples were fabricated using three different cell lines; PC12, NT2/D1 and iPSC. PC12 cells are a rat cell line capable of differentiation into neuron-like cells NT2/D1 cells are a human cell line that exhibit biochemical and developmental properties similar to that of an early embryo and when exposed to retinoic acid the cells differentiate into human neurons useful for studies of human neurological disease. Finally induced pluripotent stem cells (iPSC) were utilized with the goal of future studies of neural networks fabricated from human iPSC derived neurons. Cells are positioned in the monomer solution with holographic optical tweezers at 1064 nm and then are encapsulated by photopolymerization of polyethylene glycol (PEG) hydrogels formed by thiol-ene photo-click chemistry via projection of a 512x512 spatial light modulator (SLM) illuminated at 405 nm. Fabricated samples are incubated in differentiation media such that cells cease to divide and begin to form axons or axon-like structures. By controlling the position of the cells within the encapsulating hydrogel structure the formation of the neural circuits is controlled. The samples fabricated with this system are a useful model for future studies of neural circuit formation, neurological disease, cellular communication, plasticity, and repair mechanisms.

  15. Neural Network Controlled Visual Saccades

    NASA Astrophysics Data System (ADS)

    Johnson, Jeffrey D.; Grogan, Timothy A.

    1989-03-01

    The paper to be presented will discuss research on a computer vision system controlled by a neural network capable of learning through classical (Pavlovian) conditioning. Through the use of unconditional stimuli (reward and punishment) the system will develop scan patterns of eye saccades necessary to differentiate and recognize members of an input set. By foveating only those portions of the input image that the system has found to be necessary for recognition the drawback of computational explosion as the size of the input image grows is avoided. The model incorporates many features found in animal vision systems, and is governed by understandable and modifiable behavior patterns similar to those reported by Pavlov in his classic study. These behavioral patterns are a result of a neuronal model, used in the network, explicitly designed to reproduce this behavior.

  16. Hand Gesture Recognition Using Neural Networks.

    DTIC Science & Technology

    1996-05-01

    inherent in the model. The high gesture recognition rates and quick network retraining times found in the present study suggest that a neural network approach to gesture recognition be further evaluated.

  17. Network measures for protein folding state discrimination

    PubMed Central

    Menichetti, Giulia; Fariselli, Piero; Remondini, Daniel

    2016-01-01

    Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis. PMID:27464796

  18. A new formulation for feedforward neural networks.

    PubMed

    Razavi, Saman; Tolson, Bryan A

    2011-10-01

    Feedforward neural network is one of the most commonly used function approximation techniques and has been applied to a wide variety of problems arising from various disciplines. However, neural networks are black-box models having multiple challenges/difficulties associated with training and generalization. This paper initially looks into the internal behavior of neural networks and develops a detailed interpretation of the neural network functional geometry. Based on this geometrical interpretation, a new set of variables describing neural networks is proposed as a more effective and geometrically interpretable alternative to the traditional set of network weights and biases. Then, this paper develops a new formulation for neural networks with respect to the newly defined variables; this reformulated neural network (ReNN) is equivalent to the common feedforward neural network but has a less complex error response surface. To demonstrate the learning ability of ReNN, in this paper, two training methods involving a derivative-based (a variation of backpropagation) and a derivative-free optimization algorithms are employed. Moreover, a new measure of regularization on the basis of the developed geometrical interpretation is proposed to evaluate and improve the generalization ability of neural networks. The value of the proposed geometrical interpretation, the ReNN approach, and the new regularization measure are demonstrated across multiple test problems. Results show that ReNN can be trained more effectively and efficiently compared to the common neural networks and the proposed regularization measure is an effective indicator of how a network would perform in terms of generalization.

  19. Extrapolation limitations of multilayer feedforward neural networks

    NASA Technical Reports Server (NTRS)

    Haley, Pamela J.; Soloway, Donald

    1992-01-01

    The limitations of backpropagation used as a function extrapolator were investigated. Four common functions were used to investigate the network's extrapolation capability. The purpose of the experiment was to determine whether neural networks are capable of extrapolation and, if so, to determine the range for which networks can extrapolate. The authors show that neural networks cannot extrapolate and offer an explanation to support this result.

  20. Problem Specific applications for Neural Networks

    DTIC Science & Technology

    1988-12-01

    97 iv List Of Figures Figure Page 1. Neural Network Models ...... ............. 2 2. A Single - Layer Perceptron ..... ........... 4...the network is in use. Three of the most well-known neural networks are the single - layer perceptron , the multi-layer perceptron, and the Kohonen self...three of these networks can accept discrete (binary) or continuous inputs (5:6). 3 Single-Laver Perceptron. The single - layer perceptron (shown in Figure 2

  1. Drift chamber tracking with neural networks

    SciTech Connect

    Lindsey, C.S.; Denby, B.; Haggerty, H.

    1992-10-01

    We discuss drift chamber tracking with a commercial log VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed.

  2. Coherence resonance in bursting neural networks.

    PubMed

    Kim, June Hoan; Lee, Ho Jun; Min, Cheol Hong; Lee, Kyoung J

    2015-10-01

    Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal-a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.

  3. Neural Network Classification of Cerebral Embolic Signals

    DTIC Science & Technology

    2007-11-02

    application of new signal processing techniques to the analysis and classification of embolic signals. We applied a Wavelet Neural Network algorithm...to approximate the embolic signals, with the parameters of the wavelet nodes being used to train a Neural Network to classify these signals as resulting from normal flow, or from gaseous or solid emboli.

  4. Multidisciplinary Studies of Integrated Neural Network Systems

    DTIC Science & Technology

    1994-03-01

    They accomplish this by partitioning the system into functional sub-units in a quasi-hierarchical structure of neural network modules. We studied...three specific examples of this system integration strategy and modeled their operation for the purpose of creating new neural network architectures and

  5. Neural Network Research: A Personal Perspective,

    DTIC Science & Technology

    1988-03-01

    These vision preprocessor and ART autonomous classifier examples are just two of the many neural network architectures now being developed by...computational theories with natural realizations as real-time adaptive neural network architectures with promising properties for tackling some of the

  6. Neural Network Based Helicopter Low Airspeed Indicator

    DTIC Science & Technology

    1996-10-24

    This invention relates generally to virtual sensors and, more particularly, to a means and method utilizing a neural network for estimating...helicopter airspeed at speeds below about 50 knots using only fixed system parameters (i.e., parameters measured or determined in a reference frame fixed relative to the helicopter fuselage) as inputs to the neural network .

  7. Evolving Neural Networks for Nonlinear Control.

    DTIC Science & Technology

    1996-09-30

    An approach to creating Amorphous Recurrent Neural Networks (ARNN) using Genetic Algorithms (GA) called 2pGA has been developed and shown to be...effective in evolving neural networks for the control and stabilization of both linear and nonlinear plants, the optimal control for a nonlinear regulator

  8. Online guidance updates using neural networks

    NASA Astrophysics Data System (ADS)

    Filici, Cristian; Sánchez Peña, Ricardo S.

    2010-02-01

    The aim of this article is to present a method for the online guidance update for a launcher ascent trajectory that is based on the utilization of a neural network approximator. Generation of training patterns and selection of the input and output spaces of the neural network are presented, and implementation issues are discussed. The method is illustrated by a 2-dimensional launcher simulation.

  9. Neural network based architectures for aerospace applications

    NASA Technical Reports Server (NTRS)

    Ricart, Richard

    1987-01-01

    A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed.

  10. 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.

  11. Self-organization of neural networks

    NASA Astrophysics Data System (ADS)

    Clark, John W.; Winston, Jeffrey V.; Rafelski, Johann

    1984-05-01

    The plastic development of a neural-network model operating autonomously in discrete time is described by the temporal modification of interneuronal coupling strengths according to momentary neural activity. A simple algorithm (“brainwashing”) is found which, applied to nets with initially quasirandom connectivity, leads to model networks with properties conductive to the simulation of memory and learning phenomena.

  12. Neural network classification - A Bayesian interpretation

    NASA Technical Reports Server (NTRS)

    Wan, Eric A.

    1990-01-01

    The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.

  13. Radiation Behavior of Analog Neural Network Chip

    NASA Technical Reports Server (NTRS)

    Langenbacher, H.; Zee, F.; Daud, T.; Thakoor, A.

    1996-01-01

    A neural network experiment conducted for the Space Technology Research Vehicle (STRV-1) 1-b launched in June 1994. Identical sets of analog feed-forward neural network chips was used to study and compare the effects of space and ground radiation on the chips. Three failure mechanisms are noted.

  14. 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.

  15. Neural Networks for Handwritten English Alphabet Recognition

    NASA Astrophysics Data System (ADS)

    Perwej, Yusuf; Chaturvedi, Ashish

    2011-04-01

    This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system.

  16. A Survey of Neural Network Publications.

    ERIC Educational Resources Information Center

    Vijayaraman, Bindiganavale S.; Osyk, Barbara

    This paper is a survey of publications on artificial neural networks published in business journals for the period ending July 1996. Its purpose is to identify and analyze trends in neural network research during that period. This paper shows which topics have been heavily researched, when these topics were researched, and how that research has…

  17. Applications of Neural Networks in Finance.

    ERIC Educational Resources Information Center

    Crockett, Henry; Morrison, Ronald

    1994-01-01

    Discusses research with neural networks in the area of finance. Highlights include bond pricing, theoretical exposition of primary bond pricing, bond pricing regression model, and an example that created networks with corporate bonds and NeuralWare Neuralworks Professional H software using the back-propagation technique. (LRW)

  18. Neural Network Algorithm for Particle Loading

    SciTech Connect

    J. L. V. Lewandowski

    2003-04-25

    An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given.

  19. 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.

  20. Neural networks applications to control and computations

    NASA Technical Reports Server (NTRS)

    Luxemburg, Leon A.

    1994-01-01

    Several interrelated problems in the area of neural network computations are described. First an interpolation problem is considered, then a control problem is reduced to a problem of interpolation by a neural network via Lyapunov function approach, and finally a new, faster method of learning as compared with the gradient descent method, was introduced.

  1. 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

  2. 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.

  3. Changes in neural network homeostasis trigger neuropsychiatric symptoms

    PubMed Central

    Winkelmann, Aline; Maggio, Nicola; Eller, Joanna; Caliskan, Gürsel; Semtner, Marcus; Häussler, Ute; Jüttner, René; Dugladze, Tamar; Smolinsky, Birthe; Kowalczyk, Sarah; Chronowska, Ewa; Schwarz, Günter; Rathjen, Fritz G.; Rechavi, Gideon; Haas, Carola A.; Kulik, Akos; Gloveli, Tengis; Heinemann, Uwe; Meier, Jochen C.

    2014-01-01

    The mechanisms that regulate the strength of synaptic transmission and intrinsic neuronal excitability are well characterized; however, the mechanisms that promote disease-causing neural network dysfunction are poorly defined. We generated mice with targeted neuron type–specific expression of a gain-of-function variant of the neurotransmitter receptor for glycine (GlyR) that is found in hippocampectomies from patients with temporal lobe epilepsy. In this mouse model, targeted expression of gain-of-function GlyR in terminals of glutamatergic cells or in parvalbumin-positive interneurons persistently altered neural network excitability. The increased network excitability associated with gain-of-function GlyR expression in glutamatergic neurons resulted in recurrent epileptiform discharge, which provoked cognitive dysfunction and memory deficits without affecting bidirectional synaptic plasticity. In contrast, decreased network excitability due to gain-of-function GlyR expression in parvalbumin-positive interneurons resulted in an anxiety phenotype, but did not affect cognitive performance or discriminative associative memory. Our animal model unveils neuron type–specific effects on cognition, formation of discriminative associative memory, and emotional behavior in vivo. Furthermore, our data identify a presynaptic disease–causing molecular mechanism that impairs homeostatic regulation of neural network excitability and triggers neuropsychiatric symptoms. PMID:24430185

  4. 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.

  5. 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…

  6. Enhancing neural-network performance via assortativity

    SciTech Connect

    Franciscis, Sebastiano de; Johnson, Samuel; Torres, Joaquin J.

    2011-03-15

    The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations - assortativity - on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

  7. Enhancing neural-network performance via assortativity.

    PubMed

    de Franciscis, Sebastiano; Johnson, Samuel; Torres, Joaquín J

    2011-03-01

    The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations--assortativity--on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

  8. Optical implementation of a feature-based neural network with application to automatic target recognition

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Stoner, William W.

    1993-01-01

    An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

  9. Automatic target recognition using a feature-based optical neural network

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin

    1992-01-01

    An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

  10. Sunspot prediction using neural networks

    NASA Technical Reports Server (NTRS)

    Villarreal, James; Baffes, Paul

    1990-01-01

    The earliest systematic observance of sunspot activity is known to have been discovered by the Chinese in 1382 during the Ming Dynasty (1368 to 1644) when spots on the sun were noticed by looking at the sun through thick, forest fire smoke. Not until after the 18th century did sunspot levels become more than a source of wonderment and curiosity. Since 1834 reliable sunspot data has been collected by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Naval Observatory. Recently, considerable effort has been placed upon the study of the effects of sunspots on the ecosystem and the space environment. The efforts of the Artificial Intelligence Section of the Mission Planning and Analysis Division of the Johnson Space Center involving the prediction of sunspot activity using neural network technologies are described.

  11. Wavelet differential neural network observer.

    PubMed

    Chairez, Isaac

    2009-09-01

    State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with a state observation problem when the dynamic model of a plant contains uncertainties or it is completely unknown. Differential neural network (NN) approach is applied in this uninformative situation but with activation functions described by wavelets. A new learning law, containing an adaptive adjustment rate, is suggested to imply the stability condition for the free parameters of the observer. Nominal weights are adjusted during the preliminary training process using the least mean square (LMS) method. Lyapunov theory is used to obtain the upper bounds for the weights dynamics as well as for the mean squared estimation error. Two numeric examples illustrate this approach: first, a nonlinear electric system, governed by the Chua's equation and second the Lorentz oscillator. Both systems are assumed to be affected by external perturbations and their parameters are unknown.

  12. Tampa Electric Neural Network Sootblowing

    SciTech Connect

    Mark A. Rhode

    2002-09-30

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NO{sub x} formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent soot-blowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, online, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce {sub x} emissions and improve heat rate

  13. Tampa Electric Neural Network Sootblowing

    SciTech Connect

    Mark A. Rhode

    2004-09-30

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NOx formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent sootblowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat rate around

  14. Tampa Electric Neural Network Sootblowing

    SciTech Connect

    Mark A. Rhode

    2004-03-31

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NOx formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing co-funding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent sootblowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat rate around

  15. Tampa Electric Neural Network Sootblowing

    SciTech Connect

    Mark A. Rhode

    2003-12-31

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NO{sub x} formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent soot-blowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat rate

  16. Neural networks for damage identification

    SciTech Connect

    Paez, T.L.; Klenke, S.E.

    1997-11-01

    Efforts to optimize the design of mechanical systems for preestablished use environments and to extend the durations of use cycles establish a need for in-service health monitoring. Numerous studies have proposed measures of structural response for the identification of structural damage, but few have suggested systematic techniques to guide the decision as to whether or not damage has occurred based on real data. Such techniques are necessary because in field applications the environments in which systems operate and the measurements that characterize system behavior are random. This paper investigates the use of artificial neural networks (ANNs) to identify damage in mechanical systems. Two probabilistic neural networks (PNNs) are developed and used to judge whether or not damage has occurred in a specific mechanical system, based on experimental measurements. The first PNN is a classical type that casts Bayesian decision analysis into an ANN framework; it uses exemplars measured from the undamaged and damaged system to establish whether system response measurements of unknown origin come from the former class (undamaged) or the latter class (damaged). The second PNN establishes the character of the undamaged system in terms of a kernel density estimator of measures of system response; when presented with system response measures of unknown origin, it makes a probabilistic judgment whether or not the data come from the undamaged population. The physical system used to carry out the experiments is an aerospace system component, and the environment used to excite the system is a stationary random vibration. The results of damage identification experiments are presented along with conclusions rating the effectiveness of the approaches.

  17. Nonlinear programming with feedforward neural networks.

    SciTech Connect

    Reifman, J.

    1999-06-02

    We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

  18. VLSI Cells Placement Using the Neural Networks

    SciTech Connect

    Azizi, Hacene; Zouaoui, Lamri; Mokhnache, Salah

    2008-06-12

    The artificial neural networks have been studied for several years. Their effectiveness makes it possible to expect high performances. The privileged fields of these techniques remain the recognition and classification. Various applications of optimization are also studied under the angle of the artificial neural networks. They make it possible to apply distributed heuristic algorithms. In this article, a solution to placement problem of the various cells at the time of the realization of an integrated circuit is proposed by using the KOHONEN network.

  19. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

    PubMed Central

    Kang, Min-Joo

    2016-01-01

    A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus. PMID:27271802

  20. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    PubMed

    Kang, Min-Joo; Kang, Je-Won

    2016-01-01

    A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

  1. Neural network regulation driven by autonomous neural firings

    NASA Astrophysics Data System (ADS)

    Cho, Myoung Won

    2016-07-01

    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  2. Object detection using pulse coupled neural networks.

    PubMed

    Ranganath, H S; Kuntimad, G

    1999-01-01

    This paper describes an object detection system based on pulse coupled neural networks. The system is designed and implemented to illustrate the power, flexibility and potential the pulse coupled neural networks have in real-time image processing. In the preprocessing stage, a pulse coupled neural network suppresses noise by smoothing the input image. In the segmentation stage, a second pulse coupled neural-network iteratively segments the input image. During each iteration, with the help of a control module, the segmentation network deletes regions that do not satisfy the retention criteria from further processing and produces an improved segmentation of the retained image. In the final stage each group of connected regions that satisfies the detection criteria is identified as an instance of the object of interest.

  3. A neural network prototyping package within IRAF

    NASA Technical Reports Server (NTRS)

    Bazell, D.; Bankman, I.

    1992-01-01

    We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

  4. Description of interatomic interactions with neural networks

    NASA Astrophysics Data System (ADS)

    Hajinazar, Samad; Shao, Junping; Kolmogorov, Aleksey N.

    Neural networks are a promising alternative to traditional classical potentials for describing interatomic interactions. Recent research in the field has demonstrated how arbitrary atomic environments can be represented with sets of general functions which serve as an input for the machine learning tool. We have implemented a neural network formalism in the MAISE package and developed a protocol for automated generation of accurate models for multi-component systems. Our tests illustrate the performance of neural networks and known classical potentials for a range of chemical compositions and atomic configurations. Supported by NSF Grant DMR-1410514.

  5. Pricing financial derivatives with neural networks

    NASA Astrophysics Data System (ADS)

    Morelli, Marco J.; Montagna, Guido; Nicrosini, Oreste; Treccani, Michele; Farina, Marco; Amato, Paolo

    2004-07-01

    Neural network algorithms are applied to the problem of option pricing and adopted to simulate the nonlinear behavior of such financial derivatives. Two different kinds of neural networks, i.e. multi-layer perceptrons and radial basis functions, are used and their performances compared in detail. The analysis is carried out both for standard European options and American ones, including evaluation of the Greek letters, necessary for hedging purposes. Detailed numerical investigation show that, after a careful phase of training, neural networks are able to predict the value of options and Greek letters with high accuracy and competitive computational time.

  6. Genetic algorithm for neural networks optimization

    NASA Astrophysics Data System (ADS)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  7. Noise cancellation of memristive neural networks.

    PubMed

    Wen, Shiping; Zeng, Zhigang; Huang, Tingwen; Yu, Xinghuo

    2014-12-01

    This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation.

  8. Multispectral image fusion using neural networks

    NASA Technical Reports Server (NTRS)

    Kagel, J. H.; Platt, C. A.; Donaven, T. W.; Samstad, E. A.

    1990-01-01

    A prototype system is being developed to demonstrate the use of neural network hardware to fuse multispectral imagery. This system consists of a neural network IC on a motherboard, a circuit card assembly, and a set of software routines hosted by a PC-class computer. Research in support of this consists of neural network simulations fusing 4 to 7 bands of Landsat imagery and fusing (separately) multiple bands of synthetic imagery. The simulations, results, and a description of the prototype system are presented.

  9. Multispectral-image fusion using neural networks

    NASA Astrophysics Data System (ADS)

    Kagel, Joseph H.; Platt, C. A.; Donaven, T. W.; Samstad, Eric A.

    1990-08-01

    A prototype system is being developed to demonstrate the use of neural network hardware to fuse multispectral imagery. This system consists of a neural network IC on a motherboard a circuit card assembly and a set of software routines hosted by a PC-class computer. Research in support of this consists of neural network simulations fusing 4 to 7 bands of Landsat imagery and fusing (separately) multiple bands of synthetic imagery. The simulations results and a description of the prototype system are presented. 1.

  10. Neural network with formed dynamics of activity

    SciTech Connect

    Dunin-Barkovskii, V.L.; Osovets, N.B.

    1995-03-01

    The problem of developing a neural network with a given pattern of the state sequence is considered. A neural network structure and an algorithm, of forming its bond matrix which lead to an approximate but robust solution of the problem are proposed and discussed. Limiting characteristics of the serviceability of the proposed structure are studied. Various methods of visualizing dynamic processes in a neural network are compared. Possible applications of the results obtained for interpretation of neurophysiological data and in neuroinformatics systems are discussed.

  11. Neural networks techniques applied to reservoir engineering

    SciTech Connect

    Flores, M.; Barragan, C.

    1995-12-31

    Neural Networks are considered the greatest technological advance since the transistor. They are expected to be a common household item by the year 2000. An attempt to apply Neural Networks to an important geothermal problem has been made, predictions on the well production and well completion during drilling in a geothermal field. This was done in Los Humeros geothermal field, using two common types of Neural Network models, available in commercial software. Results show the learning capacity of the developed model, and its precision in the predictions that were made.

  12. Stock market index prediction using neural networks

    NASA Astrophysics Data System (ADS)

    Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

    1994-03-01

    A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

  13. Threshold control of chaotic neural network.

    PubMed

    He, Guoguang; Shrimali, Manish Dev; Aihara, Kazuyuki

    2008-01-01

    The chaotic neural network constructed with chaotic neurons exhibits rich dynamic behaviour with a nonperiodic associative memory. In the chaotic neural network, however, it is difficult to distinguish the stored patterns in the output patterns because of the chaotic state of the network. In order to apply the nonperiodic associative memory into information search, pattern recognition etc. it is necessary to control chaos in the chaotic neural network. We have studied the chaotic neural network with threshold activated coupling, which provides a controlled network with associative memory dynamics. The network converges to one of its stored patterns or/and reverse patterns which has the smallest Hamming distance from the initial state of the network. The range of the threshold applied to control the neurons in the network depends on the noise level in the initial pattern and decreases with the increase of noise. The chaos control in the chaotic neural network by threshold activated coupling at varying time interval provides controlled output patterns with different temporal periods which depend upon the control parameters.

  14. Nonequilibrium landscape theory of neural networks.

    PubMed

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-11-05

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

  15. Drug use Discrimination Predicts Formation of High-Risk Social Networks: Examining Social Pathways of Discrimination.

    PubMed

    Crawford, Natalie D; Ford, Chandra; Rudolph, Abby; Kim, BoRin; Lewis, Crystal M

    2016-12-26

    Experiences of discrimination, or social marginalization and ostracism, may lead to the formation of social networks characterized by inequality. For example, those who experience discrimination may be more likely to develop drug use and sexual partnerships with others who are at increased risk for HIV compared to those without experiences of discrimination. This is critical as engaging in risk behaviors with others who are more likely to be HIV positive can increase one's risk of HIV. We used log-binomial regression models to examine the relationship between drug use, racial and incarceration discrimination with changes in the composition of one's risk network among 502 persons who use drugs. We examined both absolute and proportional changes with respect to sex partners, drug use partners, and injecting partners, after accounting for individual risk behaviors. At baseline, participants were predominately male (70%), black or Latino (91%), un-married (85%), and used crack (64%). Among those followed-up (67%), having experienced discrimination due to drug use was significantly related to increases in the absolute number of sex networks and drug networks over time. No types of discrimination were related to changes in the proportion of high-risk network members. Discrimination may increase one's risk of HIV acquisition by leading them to preferentially form risk relationships with higher-risk individuals, thereby perpetuating racial and ethnic inequities in HIV. Future social network studies and behavioral interventions should consider whether social discrimination plays a role in HIV transmission.

  16. An Introduction to Neural Networks for Hearing Aid Noise Recognition.

    ERIC Educational Resources Information Center

    Kim, Jun W.; Tyler, Richard S.

    1995-01-01

    This article introduces the use of multilayered artificial neural networks in hearing aid noise recognition. It reviews basic principles of neural networks, and offers an example of an application in which a neural network is used to identify the presence or absence of noise in speech. The ability of neural networks to "learn" the…

  17. Results of the neural network investigation

    NASA Astrophysics Data System (ADS)

    Uvanni, Lee A.

    1992-04-01

    Rome Laboratory has designed and implemented a neural network based automatic target recognition (ATR) system under contract F30602-89-C-0079 with Booz, Allen & Hamilton (BAH), Inc., of Arlington, Virginia. The system utilizes a combination of neural network paradigms and conventional image processing techniques in a parallel environment on the IE- 2000 SUN 4 workstation at Rome Laboratory. The IE-2000 workstation was designed to assist the Air Force and Department of Defense to derive the needs for image exploitation and image exploitation support for the late 1990s - year 2000 time frame. The IE-2000 consists of a developmental testbed and an applications testbed, both with the goal of solving real world problems on real-world facilities for image exploitation. To fully exploit the parallel nature of neural networks, 18 Inmos T800 transputers were utilized, in an attempt to provide a near- linear speed-up for each subsystem component implemented on them. The initial design contained three well-known neural network paradigms, each modified by BAH to some extent: the Selective Attention Neocognitron (SAN), the Binary Contour System/Feature Contour System (BCS/FCS), and Adaptive Resonance Theory 2 (ART-2), and one neural network designed by BAH called the Image Variance Exploitation Network (IVEN). Through rapid prototyping, the initial system evolved into a completely different final design, called the Neural Network Image Exploitation System (NNIES), where the final system consists of two basic components: the Double Variance (DV) layer and the Multiple Object Detection And Location System (MODALS). A rapid prototyping neural network CAD Tool, designed by Booz, Allen & Hamilton, was used to rapidly build and emulate the neural network paradigms. Evaluation of the completed ATR system included probability of detections and probability of false alarms among other measures.

  18. Recognition of Telugu characters using neural networks.

    PubMed

    Sukhaswami, M B; Seetharamulu, P; Pujari, A K

    1995-09-01

    The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

  19. Neural Networks for Dynamic Flight Control

    DTIC Science & Technology

    1993-12-01

    uses the Adaline (22) model for development of the neural networks. Neural Graphics and other AFIT applications use a slightly different model. The...primary difference in the Nguyen application is that the Adaline uses the nonlinear function .f(a) = tanh(a) where standard backprop uses the sigmoid

  20. Radar signal categorization using a neural network

    NASA Technical Reports Server (NTRS)

    Anderson, James A.; Gately, Michael T.; Penz, P. Andrew; Collins, Dean R.

    1991-01-01

    Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

  1. Constructive Autoassociative Neural Network for Facial Recognition

    PubMed Central

    Fernandes, Bruno J. T.; Cavalcanti, George D. C.; Ren, Tsang I.

    2014-01-01

    Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network). CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature. PMID:25542018

  2. Neural Network Solutions to Optical Absorption Spectra

    NASA Astrophysics Data System (ADS)

    Rosenbrock, Conrad

    2012-10-01

    Artificial neural networks have been effective in reducing computation time while achieving remarkable accuracy for a variety of difficult physics problems. Neural networks are trained iteratively by adjusting the size and shape of sums of non-linear functions by varying the function parameters to fit results for complex non-linear systems. For smaller structures, ab initio simulation methods can be used to determine absorption spectra under field perturbations. However, these methods are impractical for larger structures. Designing and training an artificial neural network with simulated data from time-dependent density functional theory may allow time-dependent perturbation effects to be calculated more efficiently. I investigate the design considerations and results of neural network implementations for calculating perturbation-coupled electron oscillations in small molecules.

  3. Imbibition well stimulation via neural network design

    DOEpatents

    Weiss, William

    2007-08-14

    A method for stimulation of hydrocarbon production via imbibition by utilization of surfactants. The method includes use of fuzzy logic and neural network architecture constructs to determine surfactant use.

  4. Temporal Coding in Realistic Neural Networks

    NASA Astrophysics Data System (ADS)

    Gerasyuta, S. M.; Ivanov, D. V.

    1995-10-01

    The modification of realistic neural network model have been proposed. The model differs from the Hopfield model because of the two characteristic contributions to synaptic efficacious: the short-time contribution which is determined by the chemical reactions in the synapses and the long-time contribution corresponding to the structural changes of synaptic contacts. The approximation solution of the realistic neural network model equations is obtained. This solution allows us to calculate the postsynaptic potential as function of input. Using the approximate solution of realistic neural network model equations the behaviour of postsynaptic potential of realistic neural network as function of time for the different temporal sequences of stimuli is described. The various outputs are obtained for the different temporal sequences of the given stimuli. These properties of the temporal coding can be exploited as a recognition element capable of being selectively tuned to different inputs.

  5. A neural network for bounded linear programming

    SciTech Connect

    Culioli, J.C.; Protopopescu, V.; Britton, C.; Ericson, N. )

    1989-01-01

    The purpose of this paper is to describe a neural network implementation of an algorithm recently designed at ORNL to solve the Transportation and the Assignment Problems, and, more generally, any explicitly bounded linear program. 9 refs.

  6. Blood glucose prediction using neural network

    NASA Astrophysics Data System (ADS)

    Soh, Chit Siang; Zhang, Xiqin; Chen, Jianhong; Raveendran, P.; Soh, Phey Hong; Yeo, Joon Hock

    2008-02-01

    We used neural network for blood glucose level determination in this study. The data set used in this study was collected using a non-invasive blood glucose monitoring system with six laser diodes, each laser diode operating at distinct near infrared wavelength between 1500nm and 1800nm. The neural network is specifically used to determine blood glucose level of one individual who participated in an oral glucose tolerance test (OGTT) session. Partial least squares regression is also used for blood glucose level determination for the purpose of comparison with the neural network model. The neural network model performs better in the prediction of blood glucose level as compared with the partial least squares model.

  7. Using Neural Networks for Sensor Validation

    NASA Technical Reports Server (NTRS)

    Mattern, Duane L.; Jaw, Link C.; Guo, Ten-Huei; Graham, Ronald; McCoy, William

    1998-01-01

    This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed.

  8. 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.

  9. Using neural networks in software repositories

    NASA Technical Reports Server (NTRS)

    Eichmann, David (Editor); Srinivas, Kankanahalli; Boetticher, G.

    1992-01-01

    The first topic is an exploration of the use of neural network techniques to improve the effectiveness of retrieval in software repositories. The second topic relates to a series of experiments conducted to evaluate the feasibility of using adaptive neural networks as a means of deriving (or more specifically, learning) measures on software. Taken together, these two efforts illuminate a very promising mechanism supporting software infrastructures - one based upon a flexible and responsive technology.

  10. Limitations of opto-electronic neural networks

    NASA Technical Reports Server (NTRS)

    Yu, Jeffrey; Johnston, Alan; Psaltis, Demetri; Brady, David

    1989-01-01

    Consideration is given to the limitations of implementing neurons, weights, and connections in neural networks for electronics and optics. It is shown that the advantages of each technology are utilized when electronically fabricated neurons are included and a combination of optics and electronics are employed for the weights and connections. The relationship between the types of neural networks being constructed and the choice of technologies to implement the weights and connections is examined.

  11. Predicting Car Production using a Neural Network

    DTIC Science & Technology

    2003-04-24

    World Almanac Education Group, 2003 [8] E. Petroutsos, Mastering Visual Basic .NET, SYBEX Inc., 2002 [9] D. E. Rumelhart, J. L. McClelland, Parallel...In this example, 100,000 cycles (epochs) were used to train it. The initial weights were randomly selected from values between 1 and -1. Visual ... basic .NET was used to program the neural network [8]. The neural network algorithm followed the steps outlined in [9]. As stated above, a 3 layer

  12. Neural network for image segmentation

    NASA Astrophysics Data System (ADS)

    Skourikhine, Alexei N.; Prasad, Lakshman; Schlei, Bernd R.

    2000-10-01

    Image analysis is an important requirement of many artificial intelligence systems. Though great effort has been devoted to inventing efficient algorithms for image analysis, there is still much work to be done. It is natural to turn to mammalian vision systems for guidance because they are the best known performers of visual tasks. The pulse- coupled neural network (PCNN) model of the cat visual cortex has proven to have interesting properties for image processing. This article describes the PCNN application to the processing of images of heterogeneous materials; specifically PCNN is applied to image denoising and image segmentation. Our results show that PCNNs do well at segmentation if we perform image smoothing prior to segmentation. We use PCNN for obth smoothing and segmentation. Combining smoothing and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. This approach makes image processing based on PCNN more automatic in our application and also results in better segmentation.

  13. 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.

  14. A neural network simulation package in CLIPS

    NASA Technical Reports Server (NTRS)

    Bhatnagar, Himanshu; Krolak, Patrick D.; Mcgee, Brenda J.; Coleman, John

    1990-01-01

    The intrinsic similarity between the firing of a rule and the firing of a neuron has been captured in this research to provide a neural network development system within an existing production system (CLIPS). A very important by-product of this research has been the emergence of an integrated technique of using rule based systems in conjunction with the neural networks to solve complex problems. The systems provides a tool kit for an integrated use of the two techniques and is also extendible to accommodate other AI techniques like the semantic networks, connectionist networks, and even the petri nets. This integrated technique can be very useful in solving complex AI problems.

  15. Logarithmic learning for generalized classifier neural network.

    PubMed

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network.

  16. Neural networks for segmentation, tracking, and identification

    NASA Astrophysics Data System (ADS)

    Rogers, Steven K.; Ruck, Dennis W.; Priddy, Kevin L.; Tarr, Gregory L.

    1992-09-01

    The main thrust of this paper is to encourage the use of neural networks to process raw data for subsequent classification. This article addresses neural network techniques for processing raw pixel information. For this paper the definition of neural networks includes the conventional artificial neural networks such as the multilayer perceptrons and also biologically inspired processing techniques. Previously, we have successfully used the biologically inspired Gabor transform to process raw pixel information and segment images. In this paper we extend those ideas to both segment and track objects in multiframe sequences. It is also desirable for the neural network processing data to learn features for subsequent recognition. A common first step for processing raw data is to transform the data and use the transform coefficients as features for recognition. For example, handwritten English characters become linearly separable in the feature space of the low frequency Fourier coefficients. Much of human visual perception can be modelled by assuming low frequency Fourier as the feature space used by the human visual system. The optimum linear transform, with respect to reconstruction, is the Karhunen-Loeve transform (KLT). It has been shown that some neural network architectures can compute approximations to the KLT. The KLT coefficients can be used for recognition as well as for compression. We tested the use of the KLT on the problem of interfacing a nonverbal patient to a computer. The KLT uses an optimal basis set for object reconstruction. For object recognition, the KLT may not be optimal.

  17. Neural-Network Object-Recognition Program

    NASA Technical Reports Server (NTRS)

    Spirkovska, L.; Reid, M. B.

    1993-01-01

    HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

  18. Fast curve fitting using neural networks

    NASA Astrophysics Data System (ADS)

    Bishop, C. M.; Roach, C. M.

    1992-10-01

    Neural networks provide a new tool for the fast solution of repetitive nonlinear curve fitting problems. In this article we introduce the concept of a neural network, and we show how such networks can be used for fitting functional forms to experimental data. The neural network algorithm is typically much faster than conventional iterative approaches. In addition, further substantial improvements in speed can be obtained by using special purpose hardware implementations of the network, thus making the technique suitable for use in fast real-time applications. The basic concepts are illustrated using a simple example from fusion research, involving the determination of spectral line parameters from measurements of B iv impurity radiation in the COMPASS-C tokamak.

  19. A neural network for visual pattern recognition

    SciTech Connect

    Fukushima, K.

    1988-03-01

    A modeling approach, which is a synthetic approach using neural network models, continues to gain importance. In the modeling approach, the authors study how to interconnect neurons to synthesize a brain model, which is a network with the same functions and abilities as the brain. The relationship between modeling neutral networks and neurophysiology resembles that between theoretical physics and experimental physics. Modeling takes synthetic approach, while neurophysiology or psychology takes an analytical approach. Modeling neural networks is useful in explaining the brain and also in engineering applications. It brings the results of neurophysiological and psychological research to engineering applications in the most direct way possible. This article discusses a neural network model thus obtained, a model with selective attention in visual pattern recognition.

  20. 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

  1. 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.

  2. Hardware implementation of stochastic spiking neural networks.

    PubMed

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  3. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    PubMed

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2016-10-20

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  4. Optical neural stimulation modeling on degenerative neocortical neural networks

    NASA Astrophysics Data System (ADS)

    Zverev, M.; Fanjul-Vélez, F.; Salas-García, I.; Arce-Diego, J. L.

    2015-07-01

    Neurodegenerative diseases usually appear at advanced age. Medical advances make people live longer and as a consequence, the number of neurodegenerative diseases continuously grows. There is still no cure for these diseases, but several brain stimulation techniques have been proposed to improve patients' condition. One of them is Optical Neural Stimulation (ONS), which is based on the application of optical radiation over specific brain regions. The outer cerebral zones can be noninvasively stimulated, without the common drawbacks associated to surgical procedures. This work focuses on the analysis of ONS effects in stimulated neurons to determine their influence in neuronal activity. For this purpose a neural network model has been employed. The results show the neural network behavior when the stimulation is provided by means of different optical radiation sources and constitute a first approach to adjust the optical light source parameters to stimulate specific neocortical areas.

  5. ARTMAP and orthonormal basis function neural networks for pattern classification

    NASA Astrophysics Data System (ADS)

    Shock, Byron Mitchell

    This dissertation investigates neural network approaches to pattern classification. One application considered is the classification of land use change in the Nile River delta between 1984 and 1993 from ten Landsat Thematic Mapper (Landsat TM) images acquired during this period. Other applications, including image segmentation, letter recognition, and prediction of variables from census data, are represented by the standardized DELVE (Data for Evaluating Learning in Valid Experiments) machine learning database. An ARTMAP (Adaptive Resonance Theory Map) neural network system is developed for the land use change classification task. Cross-validation is used to enable design decisions and to enable model fitting to be done without regard to data in test partitions. The training of voting ARTMAP systems on brightness-greenness-wetness (BGW) data for multiple dates and location data results in performance competitive with previously used expert systems. Orthonormal basis function classification methods are extended to make them appropriate for multidimensional problems. These methods share the multilayer perceptron architecture common to many neural networks. A layer of basis functions transforms the data prior to classification. Stopping rules are used to determine which basis functions to include in a model to minimize the expected mean integrated squared error (MISE). To perform stopping when using the discriminant function of Devroye et al. (1996), an appropriate MISE estimator is developed. Linear transformations to rotate data and improve multiple classification results are investigated using development benchmarks from the DELVE suite. Orthonormal basis function neural network classifiers using these principles are developed and tested along with standard pattern classification techniques on the DELVE suite. Orthonormal basis function systems appear to be well suited for some multidimensional problems. These systems, along with benchmark classifiers, are also

  6. Sequential state generation by model neural networks.

    PubMed Central

    Kleinfeld, D

    1986-01-01

    Sequential patterns of neural output activity form the basis of many biological processes, such as the cyclic pattern of outputs that control locomotion. I show how such sequences can be generated by a class of model neural networks that make defined sets of transitions between selected memory states. Sequence-generating networks depend upon the interplay between two sets of synaptic connections. One set acts to stabilize the network in its current memory state, while the second set, whose action is delayed in time, causes the network to make specified transitions between the memories. The dynamic properties of these networks are described in terms of motion along an energy surface. The performance of the networks, both with intact connections and with noisy or missing connections, is illustrated by numerical examples. In addition, I present a scheme for the recognition of externally generated sequences by these networks. PMID:3467316

  7. 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%.

  8. Some neural networks compute, others don't.

    PubMed

    Piccinini, Gualtiero

    2008-01-01

    I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend the following theses. (1) Many neural networks compute--they perform computations. (2) Some neural networks compute in a classical way. Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks compute in a non-classical way. (4) Yet other neural networks do not perform computations. Brains may well fall into this last class.

  9. On sparsely connected optimal neural networks

    SciTech Connect

    Beiu, V.; Draghici, S.

    1997-10-01

    This paper uses two different approaches to show that VLSI- and size-optimal discrete neural networks are obtained for small fan-in values. These have applications to hardware implementations of neural networks, but also reveal an intrinsic limitation of digital VLSI technology: its inability to cope with highly connected structures. The first approach is based on implementing F{sub n,m} functions. The authors show that this class of functions can be implemented in VLSI-optimal (i.e., minimizing AT{sup 2}) neural networks of small constant fan-ins. In order to estimate the area (A) and the delay (T) of such networks, the following cost functions will be used: (i) the connectivity and the number-of-bits for representing the weights and thresholds--for good estimates of the area; and (ii) the fan-ins and the length of the wires--for good approximates of the delay. The second approach is based on implementing Boolean functions for which the classical Shannon`s decomposition can be used. Such a solution has already been used to prove bounds on the size of fan-in 2 neural networks. They will generalize the result presented there to arbitrary fan-in, and prove that the size is minimized by small fan-in values. Finally, a size-optimal neural network of small constant fan-ins will be suggested for F{sub n,m} functions.

  10. Computational inference of neural information flow networks.

    PubMed

    Smith, V Anne; Yu, Jing; Smulders, Tom V; Hartemink, Alexander J; Jarvis, Erich D

    2006-11-24

    Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

  11. 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…

  12. Higher-Order Neural Networks Recognize Patterns

    NASA Technical Reports Server (NTRS)

    Reid, Max B.; Spirkovska, Lilly; Ochoa, Ellen

    1996-01-01

    Networks of higher order have enhanced capabilities to distinguish between different two-dimensional patterns and to recognize those patterns. Also enhanced capabilities to "learn" patterns to be recognized: "trained" with far fewer examples and, therefore, in less time than necessary to train comparable first-order neural networks.

  13. Orthogonal Patterns In A Binary Neural Network

    NASA Technical Reports Server (NTRS)

    Baram, Yoram

    1991-01-01

    Report presents some recent developments in theory of binary neural networks. Subject matter relevant to associate (content-addressable) memories and to recognition of patterns - both of considerable importance in advancement of robotics and artificial intelligence. When probed by any pattern, network converges to one of stored patterns.

  14. Neural-Network Modeling Of Arc Welding

    NASA Technical Reports Server (NTRS)

    Anderson, Kristinn; Barnett, Robert J.; Springfield, James F.; Cook, George E.; Strauss, Alvin M.; Bjorgvinsson, Jon B.

    1994-01-01

    Artificial neural networks considered for use in monitoring and controlling gas/tungsten arc-welding processes. Relatively simple network, using 4 welding equipment parameters as inputs, estimates 2 critical weld-bead paramaters within 5 percent. Advantage is computational efficiency.

  15. Neural networks as perpetual information generators

    NASA Astrophysics Data System (ADS)

    Englisch, Harald; Xiao, Yegao; Yao, Kailun

    1991-07-01

    The information gain in a neural network cannot be larger than the bit capacity of the synapses. It is shown that the equation derived by Engel et al. [Phys. Rev. A 42, 4998 (1990)] for the strongly diluted network with persistent stimuli contradicts this condition. Furthermore, for any time step the correct equation is derived by taking the correlation between random variables into account.

  16. 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.

  17. Electronic device aspects of neural network memories

    NASA Technical Reports Server (NTRS)

    Lambe, J.; Moopenn, A.; Thakoor, A. P.

    1985-01-01

    The basic issues related to the electronic implementation of the neural network model (NNM) for content addressable memories are examined. A brief introduction to the principles of the NNM is followed by an analysis of the information storage of the neural network in the form of a binary connection matrix and the recall capability of such matrix memories based on a hardware simulation study. In addition, materials and device architecture issues involved in the future realization of such networks in VLSI-compatible ultrahigh-density memories are considered. A possible space application of such devices would be in the area of large-scale information storage without mechanical devices.

  18. Disruption forecasting at JET using neural networks

    NASA Astrophysics Data System (ADS)

    Cannas, B.; Fanni, A.; Marongiu, E.; Sonato, P.

    2004-01-01

    Neural networks are trained to evaluate the risk of plasma disruptions in a tokamak experiment using several diagnostic signals as inputs. A saliency analysis confirms the goodness of the chosen inputs, all of which contribute to the network performance. Tests that were carried out refer to data collected from succesfully terminated and disruption terminated pulses performed during two years of JET tokamak experiments. Results show the possibility of developing a neural network predictor that intervenes well in advance in order to avoid plasma disruption or mitigate its effects.

  19. Discrimination of coupling structures using causality networks from multivariate time series

    NASA Astrophysics Data System (ADS)

    Koutlis, Christos; Kugiumtzis, Dimitris

    2016-09-01

    Measures of Granger causality on multivariate time series have been used to form the so-called causality networks. A causality network represents the interdependence structure of the underlying dynamical system or coupled dynamical systems, and its properties are quantified by network indices. In this work, it is investigated whether network indices on networks generated by an appropriate Granger causality measure can discriminate different coupling structures. The information based Granger causality measure of partial mutual information from mixed embedding (PMIME) is used to form causality networks, and a large number of network indices are ranked according to their ability to discriminate the different coupling structures. The evaluation of the network indices is done with a simulation study based on two dynamical systems, the coupled Mackey-Glass delay differential equations and the neural mass model, both of 25 variables, and three prototypes of coupling structures, i.e., random, small-world, and scale-free. It is concluded that the setting of PMIME combined with a network index attains high level of discrimination of the coupling structures solely on the basis of the observed multivariate time series. This approach is demonstrated to identify epileptic seizures emerging during electroencephalogram recordings.

  20. Multiwavelet neural network and its approximation properties.

    PubMed

    Jiao, L; Pan, J; Fang, Y

    2001-01-01

    A model of multiwavelet-based neural networks is proposed. Its universal and L(2) approximation properties, together with its consistency are proved, and the convergence rates associated with these properties are estimated. The structure of this network is similar to that of the wavelet network, except that the orthonormal scaling functions are replaced by orthonormal multiscaling functions. The theoretical analyses show that the multiwavelet network converges more rapidly than the wavelet network, especially for smooth functions. To make a comparison between both networks, experiments are carried out with the Lemarie-Meyer wavelet network, the Daubechies2 wavelet network and the GHM multiwavelet network, and the results support the theoretical analysis well. In addition, the results also illustrate that at the jump discontinuities, the approximation performance of the two networks are about the same.

  1. Applications of Neural Networks to Adaptive Control

    DTIC Science & Technology

    1989-12-01

    DTIC ;- E py 00 NAVAL POSTGRADUATE SCHOOL Monterey, California I.$ RDTIC IELECTE fl THESIS BEG7V°U APPLICATIONS OF NEURAL NETWORKS TO ADAPTIVE CONTROL...Second keader E . Robert Wood, Chairman, Department of Aeronautics and Astronautics Gordoii E . Schacher, Dean of Faculty and Graduate Education ii ABSTRACT...23: Network Dynamic Stability for q(t) . ............................. 55 ix Figure 24: Network Dynamic Stability for e (t

  2. Using Neural Networks to Describe Tracer Correlations

    NASA Technical Reports Server (NTRS)

    Lary, D. J.; Mueller, M. D.; Mussa, H. Y.

    2003-01-01

    Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation co- efficient of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4, (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

  3. Learning and diagnosing faults using neural networks

    NASA Technical Reports Server (NTRS)

    Whitehead, Bruce A.; Kiech, Earl L.; Ali, Moonis

    1990-01-01

    Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

  4. Neural network technologies for image classification

    NASA Astrophysics Data System (ADS)

    Korikov, A. M.; Tungusova, A. V.

    2015-11-01

    We analyze the classes of problems with an objective necessity to use neural network technologies, i.e. representation and resolution problems in the neural network logical basis. Among these problems, image recognition takes an important place, in particular the classification of multi-dimensional data based on information about textural characteristics. These problems occur in aerospace and seismic monitoring, materials science, medicine and other. We reviewed different approaches for the texture description: statistical, structural, and spectral. We developed a neural network technology for resolving a practical problem of cloud image classification for satellite snapshots from the spectroradiometer MODIS. The cloud texture is described by the statistical characteristics of the GLCM (Gray Level Co- Occurrence Matrix) method. From the range of neural network models that might be applied for image classification, we chose the probabilistic neural network model (PNN) and developed an implementation which performs the classification of the main types and subtypes of clouds. Also, we chose experimentally the optimal architecture and parameters for the PNN model which is used for image classification.

  5. Neurale Netwerken en Radarsystemen (Neural Networks and Radar Systems)

    DTIC Science & Technology

    1989-08-01

    general issues in cognitive science", Parallel distributed processing, Vol 1: Foundations, Rumelhart et al. 1986 pp 110-146 THO rapport Pagina 151 36 D.E...34Neural networks (part 2)",Expert Focus, IEEE Expert, Spring 1988. 61 J.A. Anderson, " Cognitive and Psychological Computations with Neural Models", IEEE...Pagina 154 69 David H. Ackley, Geoffrey E. Hinton and Terrence J. Sejnowski, "A Learning Algorithm for Boltzmann machines", cognitive science 9, 147-169

  6. Estimates on compressed neural networks regression.

    PubMed

    Zhang, Yongquan; Li, Youmei; Sun, Jianyong; Ji, Jiabing

    2015-03-01

    When the neural element number n of neural networks is larger than the sample size m, the overfitting problem arises since there are more parameters than actual data (more variable than constraints). In order to overcome the overfitting problem, we propose to reduce the number of neural elements by using compressed projection A which does not need to satisfy the condition of Restricted Isometric Property (RIP). By applying probability inequalities and approximation properties of the feedforward neural networks (FNNs), we prove that solving the FNNs regression learning algorithm in the compressed domain instead of the original domain reduces the sample error at the price of an increased (but controlled) approximation error, where the covering number theory is used to estimate the excess error, and an upper bound of the excess error is given.

  7. A Recurrent Probabilistic Neural Network with Dimensional Reduction and Its Application to Time Series EEG Discrmination

    NASA Astrophysics Data System (ADS)

    Shima, Keisuke; Takata, Daisuke; Bu, Nan; Tsuji, Toshio

    This paper proposes a novel reduced-dimensional recurrent probabilistic neural network, and tries to classify electroencephalography (EEG) during motor images. In general, a recurrent probabilistic neural network (RPNN) is a useful tool for pattern discrimination of biological signals such as electromyograms (EMGs) and EEG due to its learning ability. However, when dealing with high dimensional data, RPNNs usually have problems of heavy computation burden and difficulty in training. To overcome these problems, the proposed RPNNs incorporates a dimension-reducing stage based on linear discriminant analysis into the network structure, and a hidden Markov model (HMM) and a Gaussian mixture model (GMM) are composed in the network structure for time-series discrimination. The proposed network is also applied to EEG discrimination using Laplacian filtering and wavelet packet transform (WPT). Discrimination experiments of EEG signals measured during calling motor images in mind were conducted with four subjects. The results showed that the proposed method can achieve relatively high discrimination performance (average discrimination rates: 84.6±5.9%), and indicated that the method has possibility to be applied for the human-machine interfaces.

  8. Flexible body control using neural networks

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1992-01-01

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

  9. 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.

  10. Implementing Signature Neural Networks with Spiking Neurons

    PubMed Central

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the

  11. Implementing Signature Neural Networks with Spiking Neurons.

    PubMed

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  12. Training Deep Spiking Neural Networks Using Backpropagation

    PubMed Central

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations. PMID:27877107

  13. Foreign currency rate forecasting using neural networks

    NASA Astrophysics Data System (ADS)

    Pandya, Abhijit S.; Kondo, Tadashi; Talati, Amit; Jayadevappa, Suryaprasad

    2000-03-01

    Neural networks are increasingly being used as a forecasting tool in many forecasting problems. This paper discusses the application of neural networks in predicting daily foreign exchange rates between the USD, GBP as well as DEM. We approach the problem from a time-series analysis framework - where future exchange rates are forecasted solely using past exchange rates. This relies on the belief that the past prices and future prices are very close related, and interdependent. We present the result of training a neural network with historical USD-GBP data. The methodology used in explained, as well as the training process. We discuss the selection of inputs to the network, and present a comparison of using the actual exchange rates and the exchange rate differences as inputs. Price and rate differences are the preferred way of training neural network in financial applications. Results of both approaches are present together for comparison. We show that the network is able to learn the trends in the exchange rate movements correctly, and present the results of the prediction over several periods of time.

  14. Training Deep Spiking Neural Networks Using Backpropagation.

    PubMed

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  15. Neural Correlates of Infant Accent Discrimination: An fNIRS Study

    ERIC Educational Resources Information Center

    Cristia, Alejandrina; Minagawa-Kawai, Yasuyo; Egorova, Natalia; Gervain, Judit; Filippin, Luca; Cabrol, Dominique; Dupoux, Emmanuel

    2014-01-01

    The present study investigated the neural correlates of infant discrimination of very similar linguistic varieties (Quebecois and Parisian French) using functional Near InfraRed Spectroscopy. In line with previous behavioral and electrophysiological data, there was no evidence that 3-month-olds discriminated the two regional accents, whereas…

  16. Colonoscopic polyp detection using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Park, Sun Young; Sargent, Dusty

    2016-03-01

    Computer aided diagnosis (CAD) systems for medical image analysis rely on accurate and efficient feature extraction methods. Regardless of which type of classifier is used, the results will be limited if the input features are not diagnostically relevant and do not properly discriminate between the different classes of images. Thus, a large amount of research has been dedicated to creating feature sets that capture the salient features that physicians are able to observe in the images. Successful feature extraction reduces the semantic gap between the physician's interpretation and the computer representation of images, and helps to reduce the variability in diagnosis between physicians. Due to the complexity of many medical image classification tasks, feature extraction for each problem often requires domainspecific knowledge and a carefully constructed feature set for the specific type of images being classified. In this paper, we describe a method for automatic diagnostic feature extraction from colonoscopy images that may have general application and require a lower level of domain-specific knowledge. The work in this paper expands on our previous CAD algorithm for detecting polyps in colonoscopy video. In that work, we applied an eigenimage model to extract features representing polyps, normal tissue, diverticula, etc. from colonoscopy videos taken from various viewing angles and imaging conditions. Classification was performed using a conditional random field (CRF) model that accounted for the spatial and temporal adjacency relationships present in colonoscopy video. In this paper, we replace the eigenimage feature descriptor with features extracted from a convolutional neural network (CNN) trained to recognize the same image types in colonoscopy video. The CNN-derived features show greater invariance to viewing angles and image quality factors when compared to the eigenimage model. The CNN features are used as input to the CRF classifier as before. We report

  17. Neural network approaches for noisy language modeling.

    PubMed

    Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid

    2013-11-01

    Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.

  18. 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.

  19. Signal dispersion within a hippocampal neural network

    NASA Technical Reports Server (NTRS)

    Horowitz, J. M.; Mates, J. W. B.

    1975-01-01

    A model network is described, representing two neural populations coupled so that one population is inhibited by activity it excites in the other. Parameters and operations within the model represent EPSPs, IPSPs, neural thresholds, conduction delays, background activity and spatial and temporal dispersion of signals passing from one population to the other. Simulations of single-shock and pulse-train driving of the network are presented for various parameter values. Neuronal events from 100 to 300 msec following stimulation are given special consideration in model calculations.

  20. Intelligent neural network classifier for automatic testing

    NASA Astrophysics Data System (ADS)

    Bai, Baoxing; Yu, Heping

    1996-10-01

    This paper is concerned with an application of a multilayer feedforward neural network for the vision detection of industrial pictures, and introduces a high characteristics image processing and recognizing system which can be used for real-time testing blemishes, streaks and cracks, etc. on the inner walls of high-accuracy pipes. To take full advantage of the functions of the artificial neural network, such as the information distributed memory, large scale self-adapting parallel processing, high fault-tolerance ability, this system uses a multilayer perceptron as a regular detector to extract features of the images to be inspected and classify them.

  1. Implementation aspects of Graph Neural Networks

    NASA Astrophysics Data System (ADS)

    Barcz, A.; Szymański, Z.; Jankowski, S.

    2013-10-01

    This article summarises the results of implementation of a Graph Neural Network classi er. The Graph Neural Network model is a connectionist model, capable of processing various types of structured data, including non- positional and cyclic graphs. In order to operate correctly, the GNN model must implement a transition function being a contraction map, which is assured by imposing a penalty on model weights. This article presents research results concerning the impact of the penalty parameter on the model training process and the practical decisions that were made during the GNN implementation process.

  2. 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.

  3. Simulation of photosynthetic production using neural network

    NASA Astrophysics Data System (ADS)

    Kmet, Tibor; Kmetova, Maria

    2013-10-01

    This paper deals with neural network based optimal control synthesis for solving optimal control problems with control and state constraints and discrete time delay. The optimal control problem is transcribed into nonlinear programming problem which is implemented with adaptive critic neural network. This approach is applicable to a wide class of nonlinear systems. The proposed simulation methods is illustrated by the optimal control problem of photosynthetic production described by discrete time delay differential equations. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  4. Automatic identification of species with neural networks

    PubMed Central

    Jiménez-Segura, Luz Fernanda

    2014-01-01

    A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification. PMID:25392749

  5. Classification of wheat varieties by isoelectric focusing patterns of gliadins and neural network.

    PubMed

    Søndergaard, I; Jensen, K; Krath, B N

    1994-05-01

    Classification of wheat varieties, using isoelectric focusing patterns of the gliadins, image processing and neural networks, is described. The method was compared to a statistical classification method, discriminant analysis. The isoelectric point and the area of each band were calculated by image processing. Different methods of presenting the electrophoretic patterns to the neural network were studied. The most effective method was transformation of the electrophoretic pattern to a small (11 x 47 pixels) representation of the original digitized image, which was presented to the neural network as a vector. The neural network was trained with a number of patterns and tested with new patterns from different electrophoretic runs of the same wheat varieties. In this study we used ten different wheat varieties and the neural network was able to classify 95.5% of the patterns correctly. The statistical classification method classified the same data set 91.8% correctly. We conclude that both the neural network and discriminant analysis were able to classify the patterns correctly with a high degree of certainty. The patterns that were misclassified were indistinguishable by visual inspection.

  6. 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.

  7. Discriminating complex networks through supervised NDR and Bayesian classifier

    NASA Astrophysics Data System (ADS)

    Yan, Ke-Sheng; Rong, Li-Li; Yu, Kai

    2016-12-01

    Discriminating complex networks is a particularly important task for the purpose of the systematic study of networks. In order to discriminate unknown networks exactly, a large set of network measurements are needed to be taken into account for comprehensively considering network properties. However, as we demonstrate in this paper, these measurements are nonlinear correlated with each other in general, resulting in a wide variety of redundant measurements which unintentionally explain the same aspects of network properties. To solve this problem, we adopt supervised nonlinear dimensionality reduction (NDR) to eliminate the nonlinear redundancy and visualize networks in a low-dimensional projection space. Though unsupervised NDR can achieve the same aim, we illustrate that supervised NDR is more appropriate than unsupervised NDR for discrimination task. After that, we perform Bayesian classifier (BC) in the projection space to discriminate the unknown network by considering the projection score vectors as the input of the classifier. We also demonstrate the feasibility and effectivity of this proposed method in six extensive research real networks, ranging from technological to social or biological. Moreover, the effectiveness and advantage of the proposed method is proved by the contrast experiments with the existing method.

  8. Experimental fault characterization of a neural network

    NASA Technical Reports Server (NTRS)

    Tan, Chang-Huong

    1990-01-01

    The effects of a variety of faults on a neural network is quantified via simulation. The neural network consists of a single-layered clustering network and a three-layered classification network. The percentage of vectors mistagged by the clustering network, the percentage of vectors misclassified by the classification network, the time taken for the network to stabilize, and the output values are all measured. The results show that both transient and permanent faults have a significant impact on the performance of the measured network. The corresponding mistag and misclassification percentages are typically within 5 to 10 percent of each other. The average mistag percentage and the average misclassification percentage are both about 25 percent. After relearning, the percentage of misclassifications is reduced to 9 percent. In addition, transient faults are found to cause the network to be increasingly unstable as the duration of a transient is increased. The impact of link faults is relatively insignificant in comparison with node faults (1 versus 19 percent misclassified after relearning). There is a linear increase in the mistag and misclassification percentages with decreasing hardware redundancy. In addition, the mistag and misclassification percentages linearly decrease with increasing network size.

  9. Payload Invariant Control via Neural Networks: Development and Experimental Evaluation

    DTIC Science & Technology

    1989-12-01

    control is proposed and experimentally evaluated. An Adaptive Model-Based Neural Network Controller (AMBNNC) uses multilayer perceptron artificial neural ... networks to estimate the payload during high speed manipulator motion. The payload estimate adapts the feedforward compensator to unmodeled system

  10. Computational chaos in massively parallel neural networks

    NASA Technical Reports Server (NTRS)

    Barhen, Jacob; Gulati, Sandeep

    1989-01-01

    A fundamental issue which directly impacts the scalability of current theoretical neural network models to massively parallel embodiments, in both software as well as hardware, is the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the possible emergence of chaotic manifestations. Previous analyses attributed dynamical instability to the topology of the interconnection matrix, to parasitic components or to propagation delays. However, researchers have observed the existence of emergent computational chaos in a concurrently asynchronous framework, independent of the network topology. Researcher present a methodology enabling the effective asynchronous operation of large-scale neural networks. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are computed formally to characterize the underlying nonlinear dynamics. Simulation results are presented to illustrate network convergence to the correct results, even in the presence of large delays.

  11. The labeled systems of multiple neural networks.

    PubMed

    Nemissi, M; Seridi, H; Akdag, H

    2008-08-01

    This paper proposes an implementation scheme of K-class classification problem using systems of multiple neural networks. Usually, a multi-class problem is decomposed into simple sub-problems solved independently using similar single neural networks. For the reason that these sub-problems are not equivalent in their complexity, we propose a system that includes reinforced networks destined to solve complicated parts of the entire problem. Our approach is inspired from principles of the multi-classifiers systems and the labeled classification, which aims to improve performances of the networks trained by the Back-Propagation algorithm. We propose two implementation schemes based on both OAO (one-against-all) and OAA (one-against-one). The proposed models are evaluated using iris and human thigh databases.

  12. 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.

  13. A neural network based speech recognition system

    NASA Astrophysics Data System (ADS)

    Carroll, Edward J.; Coleman, Norman P., Jr.; Reddy, G. N.

    1990-02-01

    An overview is presented of the development of a neural network based speech recognition system. The two primary tasks involved were the development of a time invariant speech encoder and a pattern recognizer or detector. The speech encoder uses amplitude normalization and a Fast Fourier Transform to eliminate amplitude and frequency shifts of acoustic clues. The detector consists of a back-propagation network which accepts data from the encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection time is no more than a few network time constants, and its recognition speed is independent of the number of the words in the vocabulary. The completed system has functioned as expected with high tolerance to input variation and with error rates comparable to a commercial system when used in a noisy environment.

  14. A neural network with modular hierarchical learning

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F. (Inventor); Toomarian, Nikzad (Inventor)

    1994-01-01

    This invention provides a new hierarchical approach for supervised neural learning of time dependent trajectories. The modular hierarchical methodology leads to architectures which are more structured than fully interconnected networks. The networks utilize a general feedforward flow of information and sparse recurrent connections to achieve dynamic effects. The advantages include the sparsity of units and connections, the modular organization. A further advantage is that the learning is much more circumscribed learning than in fully interconnected systems. The present invention is embodied by a neural network including a plurality of neural modules each having a pre-established performance capability wherein each neural module has an output outputting present results of the performance capability and an input for changing the present results of the performance capabilitiy. For pattern recognition applications, the performance capability may be an oscillation capability producing a repeating wave pattern as the present results. In the preferred embodiment, each of the plurality of neural modules includes a pre-established capability portion and a performance adjustment portion connected to control the pre-established capability portion.

  15. Neural Network Noise Anomaly Recognition System and Method

    DTIC Science & Technology

    2000-10-04

    determine when an input waveform deviates from learned noise characteristics. A plurality of neural networks is preferably provided, which each receives a...plurality of samples of intervals or windows of the input waveform. Each of the neural networks produces an output based on whether an anomaly is...detected with respect to the noise, which the neural network is trained to detect. The plurality of outputs of the neural networks is preferably applied to

  16. 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

  17. Knowledge learning on fuzzy expert neural networks

    NASA Astrophysics Data System (ADS)

    Fu, Hsin-Chia; Shann, J.-J.; Pao, Hsiao-Tien

    1994-03-01

    The proposed fuzzy expert network is an event-driven, acyclic neural network designed for knowledge learning on a fuzzy expert system. Initially, the network is constructed according to a primitive (rough) expert rules including the input and output linguistic variables and values of the system. For each inference rule, it corresponds to an inference network, which contains five types of nodes: Input, Membership-Function, AND, OR, and Defuzzification Nodes. We propose a two-phase learning procedure for the inference network. The first phase is the competitive backpropagation (CBP) training phase, and the second phase is the rule- pruning phase. The CBP learning algorithm in the training phase enables the network to learn the fuzzy rules as precisely as backpropagation-type learning algorithms and yet as quickly as competitive-type learning algorithms. After the CBP training, the rule-pruning process is performed to delete redundant weight connections for simple network structures and yet compatible retrieving performance.

  18. Simplified Learning Scheme For Analog Neural Network

    NASA Technical Reports Server (NTRS)

    Eberhardt, Silvio P.

    1991-01-01

    Synaptic connections adjusted one at a time in small increments. Simplified gradient-descent learning scheme for electronic neural-network processor less efficient than better-known back-propagation scheme, but offers two advantages: easily implemented in circuitry because data-access circuitry separated from learning circuitry; and independence of data-access circuitry makes possible to implement feedforward as well as feedback networks, including those of multiple-attractor type. Important in such applications as recognition of patterns.

  19. Using neural networks to model chaos

    SciTech Connect

    Upadhyay, M.D.

    1996-12-31

    Two types of neural networks -- backpropagation and radial basis function -- are presented for modeling dynamical systems. They were trained to model the Henon, Ikeda and Tinkerbell dynamical systems by providing a set of points randomly chosen from orbits under the functions. After training, the networks were used to simulate the functions to determine the extent to which they could generate the chaotic attractors associated with these systems.

  20. Auto-associative nanoelectronic neural network

    SciTech Connect

    Nogueira, C. P. S. M.; Guimarães, J. G.

    2014-05-15

    In this paper, an auto-associative neural network using single-electron tunneling (SET) devices is proposed and simulated at low temperature. The nanoelectronic auto-associative network is able to converge to a stable state, previously stored during training. The recognition of the pattern involves decreasing the energy of the input state until it achieves a point of local minimum energy, which corresponds to one of the stored patterns.

  1. 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.

  2. Neural Network Classification of Environmental Samples

    DTIC Science & Technology

    1996-12-01

    Biological and Artificial Neural Networks. Air Force Institute of Technology, 1990. 24. Rosenblatt. Principles of Neurodynamics . New York, NY: Spartan...Parallel Distributed Processing: Explorations in the Microstructure of Cognition . MIT Press, 1986. 29. Smagt, Patrick P. Van Der. "Minimisation Methods

  3. 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…

  4. Neural network application to comprehensive engine diagnostics

    NASA Technical Reports Server (NTRS)

    Marko, Kenneth A.

    1994-01-01

    We have previously reported on the use of neural networks for detection and identification of faults in complex microprocessor controlled powertrain systems. The data analyzed in those studies consisted of the full spectrum of signals passing between the engine and the real-time microprocessor controller. The specific task of the classification system was to classify system operation as nominal or abnormal and to identify the fault present. The primary concern in earlier work was the identification of faults, in sensors or actuators in the powertrain system as it was exercised over its full operating range. The use of data from a variety of sources, each contributing some potentially useful information to the classification task, is commonly referred to as sensor fusion and typifies the type of problems successfully addressed using neural networks. In this work we explore the application of neural networks to a different diagnostic problem, the diagnosis of faults in newly manufactured engines and the utility of neural networks for process control.

  5. Multidimensional neural growing networks and computer intelligence

    SciTech Connect

    Yashchenko, V.A.

    1995-03-01

    This paper examines information-computation processes in time and in space and some aspects of computer intelligence using multidimensional matrix neural growing networks. In particular, issues of object-oriented {open_quotes}thinking{close_quotes} of computers are considered.

  6. Nonlinear Time Series Analysis via Neural Networks

    NASA Astrophysics Data System (ADS)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  7. Automatic target identification using neural networks

    NASA Astrophysics Data System (ADS)

    Abdallah, Mahmoud A.; Samu, Tayib I.; Grissom, William A.

    1995-10-01

    Neural network theories are applied to attain human-like performance in areas such as speech recognition, statistical mapping, and target recognition or identification. In target identification, one of the difficult tasks has been the extraction of features to be used to train the neural network which is subsequently used for the target's identification. The purpose of this paper is to describe the development of an automatic target identification system using features extracted from a specific class of targets. The extracted features were the graphical representations of the silhouettes of the targets. Image processing techniques and some Fast Fourier Transform (FFT) properties were implemented to extract the features. The FFT eliminates variations in the extracted features due to rotation or scaling. A Neural Network was trained with the extracted features using the Learning Vector Quantization paradigm. An identification system was set up to test the algorithm. The image processing software was interfaced with MATLAB Neural Network Toolbox via a computer program written in C language to automate the target identification process. The system performed well as at classified the objects used to train it irrespective of rotation, scaling, and translation. This automatic target identification system had a classification success rate of about 95%.

  8. Optoelectronic Integrated Circuits For Neural Networks

    NASA Technical Reports Server (NTRS)

    Psaltis, D.; Katz, J.; Kim, Jae-Hoon; Lin, S. H.; Nouhi, A.

    1990-01-01

    Many threshold devices placed on single substrate. Integrated circuits containing optoelectronic threshold elements developed for use as planar arrays of artificial neurons in research on neural-network computers. Mounted with volume holograms recorded in photorefractive crystals serving as dense arrays of variable interconnections between neurons.

  9. Neural networks in the former Soviet Union

    SciTech Connect

    Wunsch, D.C. II.

    1993-01-01

    A brief overview is given of neural networks activities in the former Soviet Union that have potential aerospace applications. Activities at institutes in Moscow, the former Leningrad, Kiev, Taganrog, Rostov-on-Don, and Krasnoyarsk are addressed, including the most important scientists involved. 21 refs.

  10. 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.

  11. 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.

  12. Neural Network Design on the SRC-6 Reconfigurable Computer

    DTIC Science & Technology

    2006-12-01

    speeds of FPGA systems. This thesis explores the use of a Feed-forward, Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) architecture... Implementation of a Fast Artificial Neural Network Library (FANN), Graduate Project Report, Department of Computer Science, University of Copenhagen (DIKU...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited NEURAL NETWORK

  13. Hyperspectral Imagery Classification Using a Backpropagation Neural Network

    DTIC Science & Technology

    1993-12-01

    A backpropagation neural network was developed and implemented for classifying AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) hyperspectral...imagery. It is a fully interconnected linkage of three layers of neural network . Fifty input layer neurons take in signals from Bands 41 to 90 of the...moderate AVIRIS pixel resolution of 20 meters by 20 meters. Backpropagation neural network , Hyperspectral imagery

  14. Electrically Modifiable Nonvolatile SONOS Synapses for Electronic Neural Networks.

    DTIC Science & Technology

    1992-09-30

    for the electrically reprogrammable analog conductance in an artificial neural network. We have demonstrated the attractive featuies of this synaptic ...Electrically Modifiable Synaptic Element for VLSI Neural Network Implementation", Proceedings of the 1991 IEEE Nonvolatile Semiconductor Memory Workshop...Nonvolatile Eletrically Modifiable Synaptic Element for VLSI Neural Network Implementation", 11th IEEE Nonvolatile Semiconductor Memory Workshop, 1991. 19. A

  15. [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.

  16. Brain tumor grading based on Neural Networks and Convolutional Neural Networks.

    PubMed

    Yuehao Pan; Weimin Huang; Zhiping Lin; Wanzheng Zhu; Jiayin Zhou; Wong, Jocelyn; Zhongxiang Ding

    2015-08-01

    This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks.

  17. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    NASA Astrophysics Data System (ADS)

    Vargas, Lorena P.; Barba, Leiner; Torres, C. O.; Mattos, L.

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  18. Temporal Evolution Of Information In Neural Networks With Feedback

    NASA Astrophysics Data System (ADS)

    Giahi Saravani, Aram; Pitkow, Xaq

    2015-03-01

    Recurrent neural networks are pivotal for information processing in the brain. Here we analyze how the information content of a neural population is altered by dynamic feedback of a stimulus estimated from the network activity. We find that the temporal evolution of the Fisher information in the model with feedback is bounded by the Fisher information in a network of pure integrators. The available information in the feedback model saturates with a time constant and to a final level both determined by the match between the estimator weights and the feedback weights. This network then encodes signals specifically from either the beginning or the end of the stimulus presentation, depending on this match. These results are relevant to recent experimental measurements of psychophysical kernels indicating that earlier stimuli have a stronger influence on perceptual discriminations than more recent stimuli. We discuss consequences of this description for choice correlations, a measure of how individual neuronal responses relate to perceptual estimates. McNair Foundation, Baylor College of Medicine, Rice University.

  19. Hybrid neural networks--combining abstract and realistic neural units.

    PubMed

    Lytton, William W; Hines, Michael

    2004-01-01

    There is a trade-off in neural network simulation between simulations that embody the details of neuronal biology and those that omit these details in favor of abstractions. The former approach appeals to physiologists and pharmacologists who can directly relate their experimental manipulations to parameter changes in the model. The latter approach appeals to physicists and mathematicians who seek analytic understanding of the behavior of large numbers of coupled simple units. This simplified approach is also valuable for practical reasons a highly simplified unit will run several orders of magnitude faster than a complex, biologically realistic unit. In order to have our cake and eat it, we have developed hybrid networks in the Neuron simulator package. These make use of Neuron's local variable timestep method to permit simplified integrate-and-fire units to move ahead quickly while realistic neurons in the same network are integrated slowly.

  20. Optical implementation of neural networks

    NASA Astrophysics Data System (ADS)

    Yu, Francis T. S.; Guo, Ruyan

    2002-12-01

    An adaptive optical neuro-computing (ONC) using inexpensive pocket size liquid crystal televisions (LCTVs) had been developed by the graduate students in the Electro-Optics Laboratory at The Pennsylvania State University. Although this neuro-computing has only 8×8=64 neurons, it can be easily extended to 16×20=320 neurons. The major advantages of this LCTV architecture as compared with other reported ONCs, are low cost and the flexibility to operate. To test the performance, several neural net models are used. These models are Interpattern Association, Hetero-association and unsupervised learning algorithms. The system design considerations and experimental demonstrations are also included.

  1. Intrinsic adaptation in autonomous recurrent neural networks.

    PubMed

    Marković, Dimitrije; Gros, Claudius

    2012-02-01

    A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depend crucially on the quality of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting, or chaotic activity patterns. We study the influence of nonsynaptic plasticity on the default dynamical state of recurrent neural networks. The nonsynaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes: a regular synchronized, an overall chaotic, and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interceded by chaotic bursts that respond sensitively to input signals. We discuss these findings in the context of self-organized information processing and critical brain dynamics.

  2. Neural networks in windprofiler data processing

    NASA Astrophysics Data System (ADS)

    Weber, H.; Richner, H.; Kretzschmar, R.; Ruffieux, D.

    2003-04-01

    Wind profilers are basically Doppler radars yielding 3-dimensional wind profiles that are deduced from the Doppler shift caused by turbulent elements in the atmosphere. These signals can be contaminated by other airborne elements such as birds or hydrometeors. Using a feed-forward neural network with one hidden layer and one output unit, birds and hydrometeors can be successfully identified in non-averaged single spectra; theses are subsequently removed in the wind computation. An infrared camera was used to identify birds in one of the beams of the wind profiler. After training the network with about 6000 contaminated data sets, it was able to identify contaminated data in a test data set with a reliability of 96 percent. The assumption was made that the neural network parameters obtained in the beam for which bird data was collected can be transferred to the other beams (at least three beams are needed for computing wind vectors). Comparing the evolution of a wind field with and without the neural network shows a significant improvement of wind data quality. Current work concentrates on training the network also for hydrometeors. It is hoped that the instrument's capability can thus be expanded to measure not only correct winds, but also observe bird migration, estimate precipitation and -- by combining precipitation information with vertical velocity measurement -- the monitoring of the height of the melting layer.

  3. Classifying multispectral data by neural networks

    NASA Technical Reports Server (NTRS)

    Telfer, Brian A.; Szu, Harold H.; Kiang, Richard K.

    1993-01-01

    Several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 Thematic Mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The Thematic Mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which further improvements will be measured. Improvements are underway to make use of both subpixel and superpixel (i.e. contextual or neighborhood) information in tile processing. For single pixel classification, the best neural network result is 78.7 percent, compared with 71.7 percent for a classical nearest neighbor classifier. The 78.7 percent result also improves on several earlier neural network results on this data.

  4. Back propagation neural networks for facial verification

    SciTech Connect

    Garnett, A.E.; Solheim, I.; Payne, T.; Castain, R.H.

    1992-10-01

    We conducted a test to determine the aptitude of neural networks to recognize human faces. The pictures we collected of 511 subjects captured both profiles and many natural expressions. Some of the subjects were wearing glasses, sunglasses, or hats in some of the pictures. The images were compressed by a factor of 100 and converted into image vectors of 1400 pixels. The image vectors were fed into a back propagation neural network with one hidden layer and one output node. The networks were trained to recognize one target person and to reject all other persons. Neural networks for 37 target subjects were trained with 8 different training sets that consisted of different subsets of the data. The networks were then tested on the rest of the data, which consisted of 7000 or more unseen pictures. Results indicate that a false acceptance rate of less than 1 percent can be obtained, and a false rejection rate of 2 percent can be obtained when certain restrictions are followed.

  5. a Heterosynaptic Learning Rule for Neural Networks

    NASA Astrophysics Data System (ADS)

    Emmert-Streib, Frank

    In this article we introduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.

  6. Fuzzy logic and neural network technologies

    NASA Technical Reports Server (NTRS)

    Villarreal, James A.; Lea, Robert N.; Savely, Robert T.

    1992-01-01

    Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.

  7. A Topological Perspective of Neural Network Structure

    NASA Astrophysics Data System (ADS)

    Sizemore, Ann; Giusti, Chad; Cieslak, Matthew; Grafton, Scott; Bassett, Danielle

    The wiring patterns of white matter tracts between brain regions inform functional capabilities of the neural network. Indeed, densely connected and cyclically arranged cognitive systems may communicate and thus perform distinctly. However, previously employed graph theoretical statistics are local in nature and thus insensitive to such global structure. Here we present an investigation of the structural neural network in eight healthy individuals using persistent homology. An extension of homology to weighted networks, persistent homology records both circuits and cliques (all-to-all connected subgraphs) through a repetitive thresholding process, thus perceiving structural motifs. We report structural features found across patients and discuss brain regions responsible for these patterns, finally considering the implications of such motifs in relation to cognitive function.

  8. Neural networks: Application to medical imaging

    NASA Technical Reports Server (NTRS)

    Clarke, Laurence P.

    1994-01-01

    The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

  9. Controlling neural network responsiveness: tradeoffs and constraints

    PubMed Central

    Keren, Hanna; Marom, Shimon

    2014-01-01

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

  10. Computationally Efficient Neural Network Intrusion Security Awareness

    SciTech Connect

    Todd Vollmer; Milos Manic

    2009-08-01

    An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.

  11. Discriminative Learning with Markov Logic Networks

    DTIC Science & Technology

    2009-10-01

    computationally expensive, especially when the number of train- ing examples are huge. One efficient alternative is online learning which processes the...first discriminative weight learner (Singla & Domingos, 2005) uses the structured perceptron algorithm (Collins, 2002) where it approximates the...These generalized clauses are scored using a chosen evaluation metric, and the clause with the best score is added to the final theory. This process is

  12. Parameterized neural networks for high-energy physics

    NASA Astrophysics Data System (ADS)

    Baldi, Pierre; Cranmer, Kyle; Faucett, Taylor; Sadowski, Peter; Whiteson, Daniel

    2016-05-01

    We investigate a new structure for machine learning classifiers built with neural networks and applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters. The physics parameters represent a smoothly varying learning task, and the resulting parameterized classifier can smoothly interpolate between them and replace sets of classifiers trained at individual values. This simplifies the training process and gives improved performance at intermediate values, even for complex problems requiring deep learning. Applications include tools parameterized in terms of theoretical model parameters, such as the mass of a particle, which allow for a single network to provide improved discrimination across a range of masses. This concept is simple to implement and allows for optimized interpolatable results.

  13. Do neural networks offer something for you?

    SciTech Connect

    Ramchandran, S.; Rhinehart, R.R.

    1995-11-01

    The concept of neural network computation was inspired by the hope to artifically reproduce some of the flexibility and power of the human brain. Human beings can recognize different patterns and voices even though these signals do not have a simple phenomenological understanding. Scientists have developed artificial neural networks (ANNs) for modeling processes that do not have a simple phenomenological explanation, such as voice recognition. Consequently, ANN jargon can be confusing to process and control engineers. In simple terms, ANNs take a nonlinear regression modeling approach. Like any regression curve-fitting approach, a least-squares optimization can generate model parameters. One advantage of ANNs is that they require neither a priori understanding of the process behavior nor phenomenological understanding of the process. ANNs use data describing the input/output relationship in a process to {open_quotes}learn{close_quotes} about the underlying process behavior. As a result of this, ANNs have a wide range of applicability. Furthermore, ANNs are computationally efficient and can replace models that are computationally intensive. This can make real-time online model-based applications practicable. A neural network is a dense mesh of nodes and connections. The basic processing elements of a network are called neurons. Neural networks are organized in layers, and typically consist of at least three layers: an input layer, one or more hidden layers, and an output layer. The input and output layers serve as interfaces that perform appropriate scaling between `real-world` and network data. Hidden layers are so termed because their neurons are hidden to the real-world data. Connections are the means for information flow. Each connection has an associated adjustable weight, w{sub i}. The weight can be regarded as a measure of the importance of the signals between the two neurons. 7 figs.

  14. Neural networks in the process industries

    SciTech Connect

    Ben, L.R.; Heavner, L.

    1996-12-01

    Neural networks, or more precisely, artificial neural networks (ANNs), are rapidly gaining in popularity. They first began to appear on the process-control scene in the early 1990s, but have been a research focus for more than 30 years. Neural networks are really empirical models that approximate the way man thinks neurons in the human brain work. Neural-net technology is not trying to produce computerized clones, but to model nature in an effort to mimic some of the brain`s capabilities. Modeling, for the purposes of this article, means developing a mathematical description of physical phenomena. The physics and chemistry of industrial processes are usually quite complex and sometimes poorly understood. Our process understanding, and our imperfect ability to describe complexity in mathematical terms, limit fidelity of first-principle models. Computational requirements for executing these complex models are a further limitation. It is often not possible to execute first-principle model algorithms at the high rate required for online control. Nevertheless, rigorous first principle models are commonplace design tools. Process control is another matter. Important model inputs are often not available as process measurements, making real-time application difficult. In fact, engineers often use models to infer unavailable measurements. 5 figs.

  15. Exceptional reducibility of complex-valued neural networks.

    PubMed

    Kobayashi, Masaki

    2010-07-01

    A neural network is referred to as minimal if it cannot reduce the number of hidden neurons that maintain the input-output map. The condition in which the number of hidden neurons can be reduced is referred to as reducibility. Real-valued neural networks have only three simple types of reducibility. It can be naturally extended to complex-valued neural networks without bias terms of hidden neurons. However, general complex-valued neural networks have another type of reducibility, referred to herein as exceptional reducibility. In this paper, another type of reducibility is presented, and a method by which to minimize complex-valued neural networks is proposed.

  16. Pruning Neural Networks with Distribution Estimation Algorithms

    SciTech Connect

    Cantu-Paz, E

    2003-01-15

    This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than the original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.

  17. Neural network computer simulation of medical aerosols.

    PubMed

    Richardson, C J; Barlow, D J

    1996-06-01

    Preliminary investigations have been conducted to assess the potential for using artificial neural networks to simulate aerosol behaviour, with a view to employing this type of methodology in the evaluation and design of pulmonary drug-delivery systems. Details are presented of the general purpose software developed for these tasks; it implements a feed-forward back-propagation algorithm with weight decay and connection pruning, the user having complete run-time control of the network architecture and mode of training. A series of exploratory investigations is then reported in which different network structures and training strategies are assessed in terms of their ability to simulate known patterns of fluid flow in simple model systems. The first of these involves simulations of cellular automata-generated data for fluid flow through a partially obstructed two-dimensional pipe. The artificial neural networks are shown to be highly successful in simulating the behaviour of this simple linear system, but with important provisos relating to the information content of the training data and the criteria used to judge when the network is properly trained. A second set of investigations is then reported in which similar networks are used to simulate patterns of fluid flow through aerosol generation devices, using training data furnished through rigorous computational fluid dynamics modelling. These more complex three-dimensional systems are modelled with equal success. It is concluded that carefully tailored, well trained networks could provide valuable tools not just for predicting but also for analysing the spatial dynamics of pharmaceutical aerosols.

  18. Membership generation using multilayer neural network

    NASA Technical Reports Server (NTRS)

    Kim, Jaeseok

    1992-01-01

    There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.

  19. Neural discriminability in rat lateral extrastriate cortex and deep but not superficial primary visual cortex correlates with shape discriminability.

    PubMed

    Vermaercke, Ben; Van den Bergh, Gert; Gerich, Florian; Op de Beeck, Hans

    2015-01-01

    Recent studies have revealed a surprising degree of functional specialization in rodent visual cortex. It is unknown to what degree this functional organization is related to the well-known hierarchical organization of the visual system in primates. We designed a study in rats that targets one of the hallmarks of the hierarchical object vision pathway in primates: selectivity for behaviorally relevant dimensions. We compared behavioral performance in a visual water maze with neural discriminability in five visual cortical areas. We tested behavioral discrimination in two independent batches of six rats using six pairs of shapes used previously to probe shape selectivity in monkey cortex (Lehky and Sereno, 2007). The relative difficulty (error rate) of shape pairs was strongly correlated between the two batches, indicating that some shape pairs were more difficult to discriminate than others. Then, we recorded in naive rats from five visual areas from primary visual cortex (V1) over areas LM, LI, LL, up to lateral occipito-temporal cortex (TO). Shape selectivity in the upper layers of V1, where the information enters cortex, correlated mostly with physical stimulus dissimilarity and not with behavioral performance. In contrast, neural discriminability in lower layers of all areas was strongly correlated with behavioral performance. These findings, in combination with the results from Vermaercke et al. (2014b), suggest that the functional specialization in rodent lateral visual cortex reflects a processing hierarchy resulting in the emergence of complex selectivity that is related to behaviorally relevant stimulus differences.

  20. 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 ,

  1. A Projection Neural Network for Constrained Quadratic Minimax Optimization.

    PubMed

    Liu, Qingshan; Wang, Jun

    2015-11-01

    This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.

  2. 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

  3. Correcting wave predictions with artificial neural networks

    NASA Astrophysics Data System (ADS)

    Makarynskyy, O.; Makarynska, D.

    2003-04-01

    The predictions of wind waves with different lead times are necessary in a large scope of coastal and open ocean activities. Numerical wave models, which usually provide this information, are based on deterministic equations that do not entirely account for the complexity and uncertainty of the wave generation and dissipation processes. An attempt to improve wave parameters short-term forecasts based on artificial neural networks is reported. In recent years, artificial neural networks have been used in a number of coastal engineering applications due to their ability to approximate the nonlinear mathematical behavior without a priori knowledge of interrelations among the elements within a system. The common multilayer feed-forward networks, with a nonlinear transfer functions in the hidden layers, were developed and employed to forecast the wave characteristics over one hour intervals starting from one up to 24 hours, and to correct these predictions. Three non-overlapping data sets of wave characteristics, both from a buoy, moored roughly 60 miles west of the Aran Islands, west coast of Ireland, were used to train and validate the neural nets involved. The networks were trained with error back propagation algorithm. Time series plots and scatterplots of the wave characteristics as well as tables with statistics show an improvement of the results achieved due to the correction procedure employed.

  4. Applications of neural networks in training science.

    PubMed

    Pfeiffer, Mark; Hohmann, Andreas

    2012-04-01

    Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming.

  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. Character Recognition Using Genetically Trained Neural Networks

    SciTech Connect

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the amount of

  7. On lateral competition in dynamic neural networks

    SciTech Connect

    Bellyustin, N.S.

    1995-02-01

    Artificial neural networks connected homogeneously, which use retinal image processing methods, are considered. We point out that there are probably two different types of lateral inhibition for each neural element by the neighboring ones-due to the negative connection coefficients between elements and due to the decreasing neuron`s response to a too high input signal. The first case characterized by stable dynamics, which is given by the Lyapunov function, while in the second case, stability is absent and two-dimensional dynamic chaos occurs if the time step in the integration of model equations is large enough. The continuous neural medium approximation is used for analytical estimation in both cases. The result is the partition of the parameter space into domains with qualitatively different dynamic modes. Computer simulations confirm the estimates and show that joining two-dimensional chaos with symmetries provided by the initial and boundary conditions may produce patterns which are genuine pieces of art.

  8. Neural networks as a control methodology

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1990-01-01

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

  9. Neural network models of categorical perception.

    PubMed

    Damper, R I; Harnad, S R

    2000-05-01

    Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

  10. A Neural Substrate for Rapid Timbre Recognition? Neural and Behavioral Discrimination of Very Brief Acoustic Vowels.

    PubMed

    Occelli, F; Suied, C; Pressnitzer, D; Edeline, J-M; Gourévitch, B

    2016-06-01

    The timbre of a sound plays an important role in our ability to discriminate between behaviorally relevant auditory categories, such as different vowels in speech. Here, we investigated, in the primary auditory cortex (A1) of anesthetized guinea pigs, the neural representation of vowels with impoverished timbre cues. Five different vowels were presented with durations ranging from 2 to 128 ms. A psychophysical experiment involving human listeners showed that identification performance was near ceiling for the longer durations and degraded close to chance level for the shortest durations. This was likely due to spectral splatter, which reduced the contrast between the spectral profiles of the vowels at short durations. Effects of vowel duration on cortical responses were well predicted by the linear frequency responses of A1 neurons. Using mutual information, we found that auditory cortical neurons in the guinea pig could be used to reliably identify several vowels for all durations. Information carried by each cortical site was low on average, but the population code was accurate even for durations where human behavioral performance was poor. These results suggest that a place population code is available at the level of A1 to encode spectral profile cues for even very short sounds.

  11. Speed up Neural Network Learning by GPGPU

    NASA Astrophysics Data System (ADS)

    Tsuchida, Yuta; Yoshioka, Michifumi

    Recently, graphic boards have higher performance with development of 3DCG and movie processing than CPU, and widely used with progress of computer entertainment. Implementation of the General-purpose computing on GPU (GPGPU) become more easier by the integrated development environment, CUDA distributed by NVIDIA. GPU has dozens or a hundred arithmetic circuits, whose allocations are controlled by CUDA. In the previous researches, the implementation of the neural network using GPGPU have been studied, however the learning of networks was not mentioned because the GPU performance is low in conditional processing whereas high in linear algebra processing. Therefore we have proposed two methods. At first, a whole network is implemented as a thread, and some networks are taught in parallel to shorten the time necessary to find the optimal weight coefficients. Secondly, this paper introduces parallelization in the neural network structure, that is, the calculation of neurons in the same layers can be paralleled. And the processes to teach for same network with different patterns are independent also. As a result, the second method is 20 times faster than CPU, and compared with the first proposed method, that is about 6 times faster.

  12. Visual grammars and their neural networks

    NASA Astrophysics Data System (ADS)

    Mjolsness, Eric

    1992-07-01

    We exhibit a systematic way to derive neural nets for vision problems. It involves formulating a vision problem as Bayesian inference or decision on a comprehensive model of the visual domain given by a probabilistic grammar. A key feature of this grammar is the way in which it eliminates model information, such as object labels, as it produces an image; correspondence problems and other noise removal tasks result. The neural nets that arise most directly are generalized assignment networks. Also there are transformations which naturally yield improved algorithms such as correlation matching in scale space and the Frameville neural nets for high-level vision. Networks derived this way generally have objective functions with spurious local minima; such minima may commonly be avoided by dynamics that include deterministic annealing, for example recent improvements to Mean Field Theory dynamics. The grammatical method of neural net design allows domain knowledge to enter from all levels of the grammar, including `abstract' levels remote from the final image data, and may permit new kinds of learning as well.

  13. Complex Chebyshev-polynomial-based unified model (CCPBUM) neural networks

    NASA Astrophysics Data System (ADS)

    Jeng, Jin-Tsong; Lee, Tsu-Tian

    1998-03-01

    In this paper, we propose complex Chebyshev Polynomial Based unified model neural network for the approximation of complex- valued function. Based on this approximate transformable technique, we have derived the relationship between the single-layered neural network and multi-layered perceptron neural network. It is shown that the complex Chebyshev Polynomial Based unified model neural network can be represented as a functional link network that are based on Chebyshev polynomial. We also derived a new learning algorithm for the proposed network. It turns out that the complex Chebyshev Polynomial Based unified model neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional complex feedforward/recurrent neural network.

  14. Automatic breast density classification using neural network

    NASA Astrophysics Data System (ADS)

    Arefan, D.; Talebpour, A.; Ahmadinejhad, N.; Kamali Asl, A.

    2015-12-01

    According to studies, the risk of breast cancer directly associated with breast density. Many researches are done on automatic diagnosis of breast density using mammography. In the current study, artifacts of mammograms are removed by using image processing techniques and by using the method presented in this study, including the diagnosis of points of the pectoral muscle edges and estimating them using regression techniques, pectoral muscle is detected with high accuracy in mammography and breast tissue is fully automatically extracted. In order to classify mammography images into three categories: Fatty, Glandular, Dense, a feature based on difference of gray-levels of hard tissue and soft tissue in mammograms has been used addition to the statistical features and a neural network classifier with a hidden layer. Image database used in this research is the mini-MIAS database and the maximum accuracy of system in classifying images has been reported 97.66% with 8 hidden layers in neural network.

  15. Application of neural networks in space construction

    NASA Technical Reports Server (NTRS)

    Thilenius, Stephen C.; Barnes, Frank

    1990-01-01

    When trying to decide what task should be done by robots and what tasks should be done by humans with respect to space construction, there has been one decisive barrier which ultimately divides the tasks: can a computer do the job? Von Neumann type computers have great difficulty with problems that the human brain seems to do instantaneously and with little effort. Some of these problems are pattern recognition, speech recognition, content addressable memories, and command interpretation. In an attempt to simulate these talents of the human brain, much research was currently done into the operations and construction of artificial neural networks. The efficiency of the interface between man and machine, robots in particular, can therefore be greatly improved with the use of neural networks. For example, wouldn't it be easier to command a robot to 'fetch an object' rather then having to remotely control the entire operation with remote control?

  16. Privacy-preserving backpropagation neural network learning.

    PubMed

    Chen, Tingting; Zhong, Sheng

    2009-10-01

    With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.

  17. Design of fiber optic adaline neural networks

    NASA Astrophysics Data System (ADS)

    Ghosh, Anjan K.; Trepka, Jim

    1997-03-01

    Based on possible optoelectronic realization of adaptive filters and equalizers using fiber optic tapped delay lines and spatial light modulators we describe the design of a single-layer fiber optic Adaline neural network that can be used as a bit pattern classifier. In our design, we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in processing speed, parallelism, and interconnection. The described new optical neural network design is for optical processing of guided light wave signals, not electronic signals. We analyze the convergence or learning characteristics of the optoelectronic Adaline in the presence of errors in the hardware. We show that with such an optoelectronic Adaline it is possible to detect a desired code word/token/header with good accuracy.

  18. Neural networks for aerosol particles characterization

    NASA Astrophysics Data System (ADS)

    Berdnik, V. V.; Loiko, V. A.

    2016-11-01

    Multilayer perceptron neural networks with one, two and three inputs are built to retrieve parameters of spherical homogeneous nonabsorbing particle. The refractive index ranges from 1.3 to 1.7; particle radius ranges from 0.251 μm to 56.234 μm. The logarithms of the scattered radiation intensity are used as input signals. The problem of the most informative scattering angles selection is elucidated. It is shown that polychromatic illumination helps one to increase significantly the retrieval accuracy. In the absence of measurement errors relative error of radius retrieval by the neural network with three inputs is 0.54%, relative error of the refractive index retrieval is 0.84%. The effect of measurement errors on the result of retrieval is simulated.

  19. 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.

  20. When Networks Disagree: Ensemble Methods for Hybrid Neural Networks

    DTIC Science & Technology

    1992-10-27

    takes the form of repeated on-line stochastic gradient descent of randomly initialized nets. However, unlike the combination process in parametric ... estimation which usually takes the form of a simple average in parameter space, the parameters in a neural network take the form of neuronal weights which

  1. Neural network error correction for solving coupled ordinary differential equations

    NASA Technical Reports Server (NTRS)

    Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

    1992-01-01

    A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

  2. Analog hardware for learning neural networks

    NASA Technical Reports Server (NTRS)

    Eberhardt, Silvio P. (Inventor)

    1991-01-01

    This is a recurrent or feedforward analog neural network processor having a multi-level neuron array and a synaptic matrix for storing weighted analog values of synaptic connection strengths which is characterized by temporarily changing one connection strength at a time to determine its effect on system output relative to the desired target. That connection strength is then adjusted based on the effect, whereby the processor is taught the correct response to training examples connection by connection.

  3. Cognitively Inspired Neural Network for Situation Recognition

    DTIC Science & Technology

    2010-01-14

    Neurodynamics of Higher-Level Cognition and Consciousness, Eds. Perlovsky, 1.I., Kozma, R. Springer Verlag, Heidelberg, Germany. Perlovsky, L.I., Deming...Perlovsky L. I., Kozma R. (2007) Eds. Neurodynamics of Higher-Level Cognition and Consciousness. Heidelberg, Germany: Springer-Verlag. Perlovsky, L.1...AFRL-RY -HS-TR-20 10-0028 Cognitively Inspired Neural Network for Situation Recognition Roman Ilin and Leonid Perlovsky AFRURYHE 80 Scott Drive

  4. Correlation Filter Synthesis Using Neural Networks.

    DTIC Science & Technology

    1993-12-01

    distortions, and this approach has clear advantages compared to searching stored filters. I)jL.,i.A I £EJTCMD 3 14. SUBJECT TERMS I NUMBER OF...distortions. They also indicate possible significant advantages compared to searching stored filters. ii 1. INTRODUCTION This section briefly...possible significant advantages compared to searching stored filters. The technical effort on correlation filter synthesis using neural networks was

  5. Program PSNN (Plasma Spectroscopy Neural Network)

    SciTech Connect

    Morgan, W.L.; Larsen, J.T.

    1993-08-01

    This program uses the standard ``delta rule`` back-propagation supervised training algorithm for multi-layer neural networks. The inputs are line intensities in arbitrary units, which are then normalized within the program. The outputs are T{sub e}(eV), N{sub e}(cm{sup {minus}3}), and a fractional ionization, which in our testing using H- and He-like spectra, was N(He)/[N(H) + N(He)].

  6. Nonvolatile Array Of Synapses For Neural Network

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1993-01-01

    Elements of array programmed with help of ultraviolet light. A 32 x 32 very-large-scale integrated-circuit array of electronic synapses serves as building-block chip for analog neural-network computer. Synaptic weights stored in nonvolatile manner. Makes information content of array invulnerable to loss of power, and, by eliminating need for circuitry to refresh volatile synaptic memory, makes architecture simpler and more compact.

  7. Learning in Neural Networks: VLSI Implementation Strategies

    NASA Technical Reports Server (NTRS)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  8. Adaptive Filtering Using Recurrent Neural Networks

    NASA Technical Reports Server (NTRS)

    Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

    2005-01-01

    A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

  9. 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.

  10. 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.

  11. Neural network architectures to analyze OPAD data

    NASA Technical Reports Server (NTRS)

    Whitaker, Kevin W.

    1992-01-01

    A prototype Optical Plume Anomaly Detection (OPAD) system is now installed on the space shuttle main engine (SSME) Technology Test Bed (TTB) at MSFC. The OPAD system requirements dictate the need for fast, efficient data processing techniques. To address this need of the OPAD system, a study was conducted into how artificial neural networks could be used to assist in the analysis of plume spectral data.

  12. Neural network with dynamically adaptable neurons

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul (Inventor)

    1994-01-01

    This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.

  13. Development and Organization of Neural Networks.

    DTIC Science & Technology

    1988-01-01

    the Hopfield relaxation model . 9 br GENERAL POTENTIAL SURFACES AN4D NEURAL NETWORKS Amir Dembo and Ofer Zeitouni Division of Applied Mathematics...Report, June 9, 1987. The Hopfield Model and Beyond, Bachmann, C. M., ARO Technical Report, December 15, 1986. A Relaxation Model for Memory with High...storage efficiencey in the Hopfield model . The original model was capable of accurate storage and retrieval, with some error correction, for up to

  14. Neural Network-Based Hyperspectral Algorithms

    DTIC Science & Technology

    2016-06-07

    our effort is development of robust numerical inversion algorithms, which will retrieve inherent optical properties of the water column as well as...combination of in-situ and model data of water column variables (IOP’s, depth, bottom type, upwelling radiance, etc.) a neural network non-linear...function approximation model will be used to establish the inverse relationship between upwelling surface radiance and the water column variables, 2

  15. Neural networks for Braille reading by the blind.

    PubMed

    Sadato, N; Pascual-Leone, A; Grafman, J; Deiber, M P; Ibañez, V; Hallett, M

    1998-07-01

    To explore the neural networks used for Braille reading, we measured regional cerebral blood flow with PET during tactile tasks performed both by Braille readers blinded early in life and by sighted subjects. Eight proficient Braille readers were studied during Braille reading with both right and left index fingers. Eight-character, non-contracted Braille-letter strings were used, and subjects were asked to discriminate between words and non-words. To compare the behaviour of the brain of the blind and the sighted directly, non-Braille tactile tasks were performed by six different blind subjects and 10 sighted control subjects using the right index finger. The tasks included a non-discrimination task and three discrimination tasks (angle, width and character). Irrespective of reading finger (right or left), Braille reading by the blind activated the inferior parietal lobule, primary visual cortex, superior occipital gyri, fusiform gyri, ventral premotor area, superior parietal lobule, cerebellum and primary sensorimotor area bilaterally, also the right dorsal premotor cortex, right middle occipital gyrus and right prefrontal area. During non-Braille discrimination tasks, in blind subjects, the ventral occipital regions, including the primary visual cortex and fusiform gyri bilaterally were activated while the secondary somatosensory area was deactivated. The reverse pattern was found in sighted subjects where the secondary somatosensory area was activated while the ventral occipital regions were suppressed. These findings suggest that the tactile processing pathways usually linked in the secondary somatosensory area are rerouted in blind subjects to the ventral occipital cortical regions originally reserved for visual shape discrimination.

  16. A space-time neural network

    NASA Technical Reports Server (NTRS)

    Villarreal, James A.; Shelton, Robert O.

    1991-01-01

    Introduced here is a novel technique which adds the dimension of time to the well known back propagation neural network algorithm. Cited here are several reasons why the inclusion of automated spatial and temporal associations are crucial to effective systems modeling. An overview of other works which also model spatiotemporal dynamics is furnished. A detailed description is given of the processes necessary to implement the space-time network algorithm. Several demonstrations that illustrate the capabilities and performance of this new architecture are given.

  17. Deep learning in neural networks: an overview.

    PubMed

    Schmidhuber, Jürgen

    2015-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

  18. Fast implementation of neural network classification

    NASA Astrophysics Data System (ADS)

    Seo, Guiwon; Ok, Jiheon; Lee, Chulhee

    2013-09-01

    Most artificial neural networks use a nonlinear activation function that includes sigmoid and hyperbolic tangent functions. Most artificial networks employ nonlinear functions such as these sigmoid and hyperbolic tangent functions, which incur high complexity costs, particularly during hardware implementation. In this paper, we propose new polynomial approximation methods for nonlinear activation functions that can substantially reduce complexity without sacrificing performance. The proposed approximation methods were applied to pattern classification problems. Experimental results show that the processing time was reduced by up to 50% without any performance degradations in terms of computer simulation.

  19. Efficient implementation of neural network deinterlacing

    NASA Astrophysics Data System (ADS)

    Seo, Guiwon; Choi, Hyunsoo; Lee, Chulhee

    2009-02-01

    Interlaced scanning has been widely used in most broadcasting systems. However, there are some undesirable artifacts such as jagged patterns, flickering, and line twitters. Moreover, most recent TV monitors utilize flat panel display technologies such as LCD or PDP monitors and these monitors require progressive formats. Consequently, the conversion of interlaced video into progressive video is required in many applications and a number of deinterlacing methods have been proposed. Recently deinterlacing methods based on neural network have been proposed with good results. On the other hand, with high resolution video contents such as HDTV, the amount of video data to be processed is very large. As a result, the processing time and hardware complexity become an important issue. In this paper, we propose an efficient implementation of neural network deinterlacing using polynomial approximation of the sigmoid function. Experimental results show that these approximations provide equivalent performance with a considerable reduction of complexity. This implementation of neural network deinterlacing can be efficiently incorporated in HW implementation.

  20. The next generation of neural network chips

    SciTech Connect

    Beiu, V.

    1997-08-01

    There have been many national and international neural networks research initiatives: USA (DARPA, NIBS), Canada (IRIS), Japan (HFSP) and Europe (BRAIN, GALA TEA, NERVES, ELENE NERVES 2) -- just to mention a few. Recent developments in the field of neural networks, cognitive science, bioengineering and electrical engineering have made it possible to understand more about the functioning of large ensembles of identical processing elements. There are more research papers than ever proposing solutions and hardware implementations are by no means an exception. Two fields (computing and neuroscience) are interacting in ways nobody could imagine just several years ago, and -- with the advent of new technologies -- researchers are focusing on trying to copy the Brain. Such an exciting confluence may quite shortly lead to revolutionary new computers and it is the aim of this invited session to bring to light some of the challenging research aspects dealing with the hardware realizability of future intelligent chips. Present-day (conventional) technology is (still) mostly digital and, thus, occupies wider areas and consumes much more power than the solutions envisaged. The innovative algorithmic and architectural ideals should represent important breakthroughs, paving the way towards making neural network chips available to the industry at competitive prices, in relatively small packages and consuming a fraction of the power required by equivalent digital solutions.

  1. Phase Transitions in Living Neural Networks

    NASA Astrophysics Data System (ADS)

    Williams-Garcia, Rashid Vladimir

    Our nervous systems are composed of intricate webs of interconnected neurons interacting in complex ways. These complex interactions result in a wide range of collective behaviors with implications for features of brain function, e.g., information processing. Under certain conditions, such interactions can drive neural network dynamics towards critical phase transitions, where power-law scaling is conjectured to allow optimal behavior. Recent experimental evidence is consistent with this idea and it seems plausible that healthy neural networks would tend towards optimality. This hypothesis, however, is based on two problematic assumptions, which I describe and for which I present alternatives in this thesis. First, critical transitions may vanish due to the influence of an environment, e.g., a sensory stimulus, and so living neural networks may be incapable of achieving "critical" optimality. I develop a framework known as quasicriticality, in which a relative optimality can be achieved depending on the strength of the environmental influence. Second, the power-law scaling supporting this hypothesis is based on statistical analysis of cascades of activity known as neuronal avalanches, which conflate causal and non-causal activity, thus confounding important dynamical information. In this thesis, I present a new method to unveil causal links, known as causal webs, between neuronal activations, thus allowing for experimental tests of the quasicriticality hypothesis and other practical applications.

  2. Constrained Least Absolute Deviation Neural Networks

    PubMed Central

    Wang, Zhishun; Peterson, Bradley S.

    2008-01-01

    It is well known that least absolute deviation (LAD) criterion or L1-norm used for estimation of parameters is characterized by robustness, i.e., the estimated parameters are totally resistant (insensitive) to large changes in the sampled data. This is an extremely useful feature, especially, when the sampled data are known to be contaminated by occasionally occurring outliers or by spiky noise. In our previous works, we have proposed the least absolute deviation neural network (LADNN) to solve unconstrained LAD problems. The theoretical proofs and numerical simulations have shown that the LADNN is Lyapunov-stable and it can globally converge to the exact solution to a given unconstrained LAD problem. We have also demonstrated its excellent application value in time-delay estimation. More generally, a practical LAD application problem may contain some linear constraints, such as a set of equalities and/or inequalities, which is called constrained LAD problem, whereas the unconstrained LAD can be considered as a special form of the constrained LAD. In this paper, we present a new neural network called constrained least absolute deviation neural network (CLADNN) to solve general constrained LAD problems. Theoretical proofs and numerical simulations demonstrate that the proposed CLADNN is Lyapunov stable and globally converges to the exact solution to a given constrained LAD problem, independent of initial values. The numerical simulations have also illustrated that the proposed CLADNN can be used to robustly estimate parameters for nonlinear curve fitting, which is extensively used in signal and image processing. PMID:18269958

  3. Random interactions in higher order neural networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre; Venkatesh, Santosh S.

    1993-01-01

    Recurrent networks of polynomial threshold elements with random symmetric interactions are studied. Precise asymptotic estimates are derived for the expected number of fixed points as a function of the margin of stability. In particular, it is shown that there is a critical range of margins of stability (depending on the degree of polynomial interaction) such that the expected number of fixed points with margins below the critical range grows exponentially with the number of nodes in the network, while the expected number of fixed points with margins above the critical range decreases exponentially with the number of nodes in the network. The random energy model is also briefly examined and links with higher order neural networks and higher order spin glass models made explicit.

  4. Segmentation of multidimensional magnetic resonance (MR) images using a fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Ma, Jesse C.; Rodriguez, Jeffrey J.

    1994-09-01

    Methods of 3-D visualization of the brain based on fuzzy c-means (FCM) classified magnetic resonance (MR) images and a neural network trained on the FCM data are presented. A 3-D MR scan of a volunteer serves as the basis for the unsupervised classification techniques. The images were first classified into different tissue types by using FCM. The classified images were then reconstructed for 3-D display. Results show that individual tissue types can be discriminated during the 3-D rendering process. A neural network trained on the fuzzy classification data was also implemented. By using the cascade correlation algorithm during the network training, much of the tedious training work was avoided. The preliminary results from the neural network approach are quite encouraging.

  5. Hidden Markov models and neural networks for fault detection in dynamic systems

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic

    1994-01-01

    Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

  6. Study on optimized Elman neural network classification algorithm based on PLS and CA.

    PubMed

    Jia, Weikuan; Zhao, Dean; Shen, Tian; Tang, Yuyang; Zhao, Yuyan

    2014-01-01

    High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion.

  7. Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA

    PubMed Central

    Zhao, Dean; Shen, Tian; Zhao, Yuyan

    2014-01-01

    High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion. PMID:25165470

  8. Desynchronization in diluted neural networks.

    PubMed

    Zillmer, Rüdiger; Livi, Roberto; Politi, Antonio; Torcini, Alessandro

    2006-09-01

    The dynamical behavior of a weakly diluted fully inhibitory network of pulse-coupled spiking neurons is investigated. Upon increasing the coupling strength, a transition from regular to stochasticlike regime is observed. In the weak-coupling phase, a periodic dynamics is rapidly approached, with all neurons firing with the same rate and mutually phase locked. The strong-coupling phase is characterized by an irregular pattern, even though the maximum Lyapunov exponent is negative. The paradox is solved by drawing an analogy with the phenomenon of "stable chaos," i.e., by observing that the stochasticlike behavior is "limited" to an exponentially long (with the system size) transient. Remarkably, the transient dynamics turns out to be stationary.

  9. Neural networks optimally trained with noisy data

    NASA Astrophysics Data System (ADS)

    Wong, K. Y. Michael; Sherrington, David

    1993-06-01

    We study the retrieval behaviors of neural networks which are trained to optimize their performance for an ensemble of noisy example patterns. In particular, we consider (1) the performance overlap, which reflects the performance of the network in an operating condition identical to the training condition; (2) the storage overlap, which reflects the ability of the network to merely memorize the stored information; (3) the attractor overlap, which reflects the precision of retrieval for dilute feedback networks; and (4) the boundary overlap, which defines the boundary of the basin of attraction, and hence the associative ability for dilute feedback networks. We find that for sufficiently low training noise, the network optimizes its overall performance by sacrificing the individual performance of a minority of patterns, resulting in a two-band distribution of the aligning fields. For a narrow range of storage level, the network loses and then regains its retrieval capability when the training noise level increases, and we interpret that this reentrant retrieval behavior is related to competing tendencies in structuring the basins of attraction for the stored patterns. Reentrant behavior is also observed in the space of synaptic interactions, in which the replica symmetric solution of the optimal network destabilizes and then restabilizes when the training noise level increases. We summarize these observations by picturing training noises as an instrument for widening the basins of attractions of the stored patterns at the expense of reducing the precision of retrieval.

  10. Models of neural networks with fuzzy activation functions

    NASA Astrophysics Data System (ADS)

    Nguyen, A. T.; Korikov, A. M.

    2017-02-01

    This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.

  11. Reducing neural network training time with parallel processing

    NASA Technical Reports Server (NTRS)

    Rogers, James L., Jr.; Lamarsh, William J., II

    1995-01-01

    Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

  12. A Wavelet Neural Network for SAR Image Segmentation

    PubMed Central

    Wen, Xian-Bin; Zhang, Hua; Wang, Fa-Yu

    2009-01-01

    This paper proposes a wavelet neural network (WNN) for SAR image segmentation by combining the wavelet transform and an artificial neural network. The WNN combines the multiscale analysis ability of the wavelet transform and the classification capability of the artificial neural network by setting the wavelet function as the transfer function of the neural network. Several SAR images are segmented by the network whose transfer functions are the Morlet and Mexihat functions, respectively. The experimental results show the proposed method is very effective and accurate. PMID:22400005

  13. A neural network short-term forecast of significant thunderstorms

    SciTech Connect

    Mccann, D.W. )

    1992-09-01

    Neural networks, an artificial-intelligence tools that excels in pattern recognition, are reviewed, and a 3-7-h significant thunderstorm forecast developed with this technique is discussed. Two neural networks learned to forecast significant thunderstorms from fields of surface-based lifted index and surface moisture convergence. These networks are sensitive to the patterns that skilled forecasters recognize as occurring prior to strong thunderstorms. The two neural networks are combined operationally at the National Severe Storm Forecast Center into a single hourly product that enhances pattern-recognition skills. Examples of neural network products are shown, and their potential impact on significant thunderstorm forecasting is demonstrated. 22 refs.

  14. Development of Neural Sensitivity to Face Identity Correlates with Perceptual Discriminability.

    PubMed

    Natu, Vaidehi S; Barnett, Michael A; Hartley, Jake; Gomez, Jesse; Stigliani, Anthony; Grill-Spector, Kalanit

    2016-10-19

    Face perception is subserved by a series of face-selective regions in the human ventral stream, which undergo prolonged development from childhood to adulthood. However, it is unknown how neural development of these regions relates to the development of face-perception abilities. Here, we used functional magnetic resonance imaging (fMRI) to measure brain responses of ventral occipitotemporal regions in children (ages, 5-12 years) and adults (ages, 19-34 years) when they viewed faces that parametrically varied in dissimilarity. Since similar faces generate lower responses than dissimilar faces due to fMRI adaptation, this design objectively evaluates neural sensitivity to face identity across development. Additionally, a subset of subjects participated in a behavioral experiment to assess perceptual discriminability of face identity. Our data reveal three main findings: (1) neural sensitivity to face identity increases with age in face-selective but not object-selective regions; (2) the amplitude of responses to faces increases with age in both face-selective and object-selective regions; and (3) perceptual discriminability of face identity is correlated with the neural sensitivity to face identity of face-selective regions. In contrast, perceptual discriminability is not correlated with the amplitude of response in face-selective regions or of responses of object-selective regions. These data suggest that developmental increases in neural sensitivity to face identity in face-selective regions improve perceptual discriminability of faces. Our findings significantly advance the understanding of the neural mechanisms of development of face perception and open new avenues for using fMRI adaptation to study the neural development of high-level visual and cognitive functions more broadly.

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

    SciTech Connect

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01

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

  16. The effect of the neural activity on topological properties of growing neural networks.

    PubMed

    Gafarov, F M; Gafarova, V R

    2016-09-01

    The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

  17. 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.

  18. Marginalization in Random Nonlinear Neural Networks

    NASA Astrophysics Data System (ADS)

    Vasudeva Raju, Rajkumar; Pitkow, Xaq

    2015-03-01

    Computations involved in tasks like causal reasoning in the brain require a type of probabilistic inference known as marginalization. Marginalization corresponds to averaging over irrelevant variables to obtain the probability of the variables of interest. This is a fundamental operation that arises whenever input stimuli depend on several variables, but only some are task-relevant. Animals often exhibit behavior consistent with marginalizing over some variables, but the neural substrate of this computation is unknown. It has been previously shown (Beck et al. 2011) that marginalization can be performed optimally by a deterministic nonlinear network that implements a quadratic interaction of neural activity with divisive normalization. We show that a simpler network can perform essentially the same computation. These Random Nonlinear Networks (RNN) are feedforward networks with one hidden layer, sigmoidal activation functions, and normally-distributed weights connecting the input and hidden layers. We train the output weights connecting the hidden units to an output population, such that the output model accurately represents a desired marginal probability distribution without significant information loss compared to optimal marginalization. Simulations for the case of linear coordinate transformations show that the RNN model has good marginalization performance, except for highly uncertain inputs that have low amplitude population responses. Behavioral experiments, based on these results, could then be used to identify if this model does indeed explain how the brain performs marginalization.

  19. Brain tumor segmentation with Deep Neural Networks.

    PubMed

    Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

    2017-01-01

    In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster.

  20. Neural Network Model of Memory Retrieval

    PubMed Central

    Recanatesi, Stefano; Katkov, Mikhail; Romani, Sandro; Tsodyks, Misha

    2015-01-01

    Human memory can store large amount of information. Nevertheless, recalling is often a challenging task. In a classical free recall paradigm, where participants are asked to repeat a briefly presented list of words, people make mistakes for lists as short as 5 words. We present a model for memory retrieval based on a Hopfield neural network where transition between items are determined by similarities in their long-term memory representations. Meanfield analysis of the model reveals stable states of the network corresponding (1) to single memory representations and (2) intersection between memory representations. We show that oscillating feedback inhibition in the presence of noise induces transitions between these states triggering the retrieval of different memories. The network dynamics qualitatively predicts the distribution of time intervals required to recall new memory items observed in experiments. It shows that items having larger number of neurons in their representation are statistically easier to recall and reveals possible bottlenecks in our ability of retrieving memories. Overall, we propose a neural network model of information retrieval broadly compatible with experimental observations and is consistent with our recent graphical model (Romani et al., 2013). PMID:26732491

  1. Electronic neural network for dynamic resource allocation

    NASA Technical Reports Server (NTRS)

    Thakoor, A. P.; Eberhardt, S. P.; Daud, T.

    1991-01-01

    A VLSI implementable neural network architecture for dynamic assignment is presented. The resource allocation problems involve assigning members of one set (e.g. resources) to those of another (e.g. consumers) such that the global 'cost' of the associations is minimized. The network consists of a matrix of sigmoidal processing elements (neurons), where the rows of the matrix represent resources and columns represent consumers. Unlike previous neural implementations, however, association costs are applied directly to the neurons, reducing connectivity of the network to VLSI-compatible 0 (number of neurons). Each row (and column) has an additional neuron associated with it to independently oversee activations of all the neurons in each row (and each column), providing a programmable 'k-winner-take-all' function. This function simultaneously enforces blocking (excitatory/inhibitory) constraints during convergence to control the number of active elements in each row and column within desired boundary conditions. Simulations show that the network, when implemented in fully parallel VLSI hardware, offers optimal (or near-optimal) solutions within only a fraction of a millisecond, for problems up to 128 resources and 128 consumers, orders of magnitude faster than conventional computing or heuristic search methods.

  2. NOVELTY DETECTION USING AUTO-ASSOCIATIVE NEURAL NETWORK

    SciTech Connect

    H. SOHN; K. WORDEN; C. FARRAR

    2001-05-01

    The primary objective of novelty detection is to examine if a system significantly deviates from the initial baseline condition of the system. In reality, the system is often subject to changing environmental and operation conditions affecting its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and humidity. Most damage diagnosis techniques, however, generally neglect the effects of these changing ambient conditions. Here, a novelty detection technique is developed explicitly taking into account these natural variations of the system in order to minimize false positive indications of true system changes. Auto-associative neural networks are employed to discriminate system changes of interest such as structural deterioration and damage from the natural variations of the system.

  3. Solving quadratic programming problems by delayed projection neural network.

    PubMed

    Yang, Yongqing; Cao, Jinde

    2006-11-01

    In this letter, the delayed projection neural network for solving convex quadratic programming problems is proposed. The neural network is proved to be globally exponentially stable and can converge to an optimal solution of the optimization problem. Three examples show the effectiveness of the proposed network.

  4. Dynamic Attractors and Basin Class Capacity in Binary Neural Networks

    DTIC Science & Technology

    1994-12-21

    The wide repertoire of attractors and basins of attraction that appear in dynamic neural networks not only serve as models of brain activity patterns...limitations of static neural networks by use of dynamic attractors and their basins. The results show that dynamic networks have a high capacity for

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  6. Nonlinear system identification and control based on modular neural networks.

    PubMed

    Puscasu, Gheorghe; Codres, Bogdan

    2011-08-01

    A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.

  7. Survey on Neural Networks Used for Medical Image Processing.

    PubMed

    Shi, Zhenghao; He, Lifeng; Suzuki, Kenji; Nakamura, Tsuyoshi; Itoh, Hidenori

    2009-02-01

    This paper aims to present a review of neural networks used in medical image processing. We classify neural networks by its processing goals and the nature of medical images. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of neural network application for medical image processing and an outlook for the future research are also discussed. By this survey, we try to answer the following two important questions: (1) What are the major applications of neural networks in medical image processing now and in the nearby future? (2) What are the major strengths and weakness of applying neural networks for solving medical image processing tasks? We believe that this would be very helpful researchers who are involved in medical image processing with neural network techniques.

  8. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

    PubMed Central

    Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

    2016-01-01

    In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423

  9. 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.

  10. ARMA Neural Networks for Predicting DGPS Pseudorange Correction

    NASA Astrophysics Data System (ADS)

    Jwo, Dah-Jing; Lee, Tai-Shen; Tseng, Ying-Wei

    2004-05-01

    In this paper, the Auto-Regressive Moving-Averaging (ARMA) neural networks (NNs) will be incorporated for predicting the differential Global Positioning System (DGPS) pseudorange correction (PRC) information. The neural network is employed to realize the time-varying ARMA implementation. Online training for real-time prediction of the PRC enhances the continuity of service on the differential correction signals and therefore improves the positioning accuracy. When the PRC signal is lost, the ARMA neural network predicted PRC would temporarily provide correction data with very good accuracy. Simulation is conducted for evaluating the ARMA NN based DGPS PRC prediction accuracy. A comparative performance study based on two types of ARMA neural networks, i.e. Back-propagation Neural Network (BPNN) and General Regression Neural Network (GRNN), will be provided.

  11. Higherorder neural network group models for financial simulation.

    PubMed

    Zhang, M; Zhang, J C; Fulcher, J

    2000-04-01

    Real world financial data is often discontinuous and non-smooth. If we attempt to use neural networks to simulate such functions, then accuracy will be a problem. Neural network group models perform this function much better. Both Polynomial Higher Order Neural network Group (PHONG) and Trigonometric polynomial Higher Order Neural network Group (THONG) models are developed. These HONG models are open box, convergent models capable of approximating any kind of piecewise continuous function, to any degree of accuracy. Moreover they are capable of handling higher frequency, higher order non-linear and discontinuous data. Results obtained using a Higher Order Neural network Group financial simulator are presented, which confirm that HONG group models converge without difficulty, and are considerably more accurate than neural network models (more specifically, around twice as good for prediction, and a factor of four improvement in the case of simulation).

  12. Three-dimensional thinning by neural networks

    NASA Astrophysics Data System (ADS)

    Shen, Jun; Shen, Wei

    1995-10-01

    3D thinning is widely used in 3D object representation in computer vision and in trajectory planning in robotics to find the topological structure of the free space. In the present paper, we propose a 3D image thinning method by neural networks. Each voxel in the 3D image corresponds to a set of neurons, called 3D Thinron, in the network. Taking the 3D Thinron as the elementary unit, the global structure of the network is a 3D array in which each Thinron is connected with the 26 neighbors in the neighborhood 3 X 3 X 3. As to the Thinron itself, the set of neurons are organized in multiple layers. In the first layer, we have neurons for boundary analysis, connectivity analysis and connectivity verification, taking as input the voxels in the 3 X 3 X 3 neighborhood and the intermediate outputs of neighboring Thinrons. In the second layer, we have the neurons for synthetical analysis to give the intermediate output of Thinron. In the third layer, we have the decision neurons whose state determines the final output. All neurons in the Thinron are the adaline neurons of Widrow, except the connectivity analysis and verification neurons which are nonlinear neurons. With the 3D Thinron neural network, the state transition of the network will take place automatically, and the network converges to the final steady state, which gives the result medial surface of 3D objects, preserving the connectivity in the initial image. The method presented is simulated and tested for 3D images, experimental results are reported.

  13. Energy coding in biological neural networks.

    PubMed

    Wang, Rubin; Zhang, Zhikang

    2007-09-01

    According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, we present a brand new scientific theory that offers an unique mechanism for brain information processing. We demonstrate that the neural coding produced by the activity of the brain is well described by our theory of energy coding. Due to the energy coding model's ability to reveal mechanisms of brain information processing based upon known biophysical properties, we can not only reproduce various experimental results of neuro-electrophysiology, but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, we estimate that the theory has very important consequences for quantitative research of cognitive function.

  14. Cognitive flexibility modulates maturation and music-training-related changes in neural sound discrimination.

    PubMed

    Saarikivi, Katri; Putkinen, Vesa; Tervaniemi, Mari; Huotilainen, Minna

    2016-07-01

    Previous research has demonstrated that musicians show superior neural sound discrimination when compared to non-musicians, and that these changes emerge with accumulation of training. Our aim was to investigate whether individual differences in executive functions predict training-related changes in neural sound discrimination. We measured event-related potentials induced by sound changes coupled with tests for executive functions in musically trained and non-trained children aged 9-11 years and 13-15 years. High performance in a set-shifting task, indexing cognitive flexibility, was linked to enhanced maturation of neural sound discrimination in both musically trained and non-trained children. Specifically, well-performing musically trained children already showed large mismatch negativity (MMN) responses at a young age as well as at an older age, indicating accurate sound discrimination. In contrast, the musically trained low-performing children still showed an increase in MMN amplitude with age, suggesting that they were behind their high-performing peers in the development of sound discrimination. In the non-trained group, in turn, only the high-performing children showed evidence of an age-related increase in MMN amplitude, and the low-performing children showed a small MMN with no age-related change. These latter results suggest an advantage in MMN development also for high-performing non-trained individuals. For the P3a amplitude, there was an age-related increase only in the children who performed well in the set-shifting task, irrespective of music training, indicating enhanced attention-related processes in these children. Thus, the current study provides the first evidence that, in children, cognitive flexibility may influence age-related and training-related plasticity of neural sound discrimination.

  15. Training product unit neural networks with genetic algorithms

    NASA Technical Reports Server (NTRS)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  16. Implementation of Fuzzy Inference Systems Using Neural Network Techniques

    DTIC Science & Technology

    1992-03-01

    rules required to implement the system, which are usually supplied by ’experts’. One alternative is to use a neural network -type architecture to implement...the fuzzy inference system, and neural network -type training techniques to ’learn’ the control parameters needed by the fuzzy inference system. By...using a generalized version of a neural network , the rules of the fuzzy inference system can be learned without the assistance of experts.

  17. 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.

  18. 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

  19. Adaptive Control of Visually Guided Grasping in Neural Networks

    DTIC Science & Technology

    1990-03-12

    U01ITU S.WM NONnumsen Adaptive Control of Visually Guided Grasping in Neural Networks AFOSR-89-&CO030 88-NL-209 L AUTHOrSF 2313/A8 00 61102F (V) Dr...FINAL REPORT ADAPTIVE CONTROL OF VISUALLY GUIDED GRASPING IN NEURAL NETWORKS Neurogen Laboratories Inc. Project Summary Research performed for AFOSR...arm’s length in position and 6 degrees in orientation. Keywords: Neural Networks , Adaptive Motor Control, Sensory-Motor sensation Introduction The human

  20. 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

  1. Equations of Learning and Capacity of Layered Neural Networks

    DTIC Science & Technology

    1989-05-01

    I-ILL ~Ut *4 (V) NWCTP 7013 (0 Equations of Learning and Capacity of 0Layered Neural Networks by Jorge M. Martin Applied Mathematics Research Group...multilayered neural networks that were discovered during the initial phase of the Independent Research project entitled "The Mathematics of Artificial...represent a small contribution to the basic knowledge of the mathematical aspects of the newly emergent theory of Artificial Neural Networks . This

  2. 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

  3. One pass learning for generalized classifier neural network.

    PubMed

    Ozyildirim, Buse Melis; Avci, Mutlu

    2016-01-01

    Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance.

  4. Scalable photonic neural networks for real-time pattern classification

    NASA Astrophysics Data System (ADS)

    Goldstein, Adam Arthur

    1997-11-01

    With the rapid advancement of photonic technology in recent years, the potential exists for the incorporation of photonic neural-network research into the development of opto-electronic real-time pattern classification systems. In this dissertation we present three classes of photonic neural-network models that were designed to be compatible with this emerging technology: (1) photonic neural networks based upon probability density estimation, (2) photorefractive neural-network models, and (3) vertically stacked photonic neural networks that utilize hybridized CMOS/GaAs chips and diffractive optical elements. In each case, we show how previously developed neural-network learning algorithms and/or architectures must be adapted in order to allow an efficient photonic implementation. For class (1), we show that conventional 'k-Nearest Neighbors' (k-NN) probability density estimation is not suitable for an analog photonic neural-network hardware implementation, and we introduce a new probability density estimation algorithm called 'Continuous k-Nearest Neighbors' (C-kNN) that is suitable. For class (2), we show that the diffraction-efficiency decay inherent to photorefractive grating formation adversely affects outer-product neural-network learning algorithms, and we introduce a gain and exposure scheduling technique that resolves the incompatibility. For class (3), the use of compact diffractive optical interconnections constrains the corresponding neural-network weights to be fixed and locally connected. We introduce a 3-D Photonic Multichip- Module (3-D PMCM) neural-network architecture that utilizes a fixed diffractive optical layer in conjunction with a programmable electronic layer, to obtain a multi- layer neural network capable of real-time pattern recognition tasks. The design and fabrication of key components of the 3-D PMCM neural-network architecture are presented, together with simulation results for the application of detecting and locating the eyes in an

  5. Position Sensorless Driving of BLDCM Using Neural Networks

    NASA Astrophysics Data System (ADS)

    Guo, Hai-Jiao; Sagawa, Seiji; Ichinokura, Osamu

    A sensorless driving method of brushless DC Motors (BLDCM) using neural network has been studied in this paper. Considering the nonlinear characteristics and the parameter error of the modeling, neural networks are introduced to estimate the electromotive force (EMF). The results of simulation and experiment using offline trained neural networks show the proposed method is useful. In addition, the robustness about the parameters is discussed.

  6. Visual processing affects the neural basis of auditory discrimination.

    PubMed

    Kislyuk, Daniel S; Möttönen, Riikka; Sams, Mikko

    2008-12-01

    The interaction between auditory and visual speech streams is a seamless and surprisingly effective process. An intriguing example is the "McGurk effect": The acoustic syllable /ba/ presented simultaneously with a mouth articulating /ga/ is typically heard as /da/ [McGurk, H., & MacDonald, J. Hearing lips and seeing voices. Nature, 264, 746-748, 1976]. Previous studies have demonstrated the interaction of auditory and visual streams at the auditory cortex level, but the importance of these interactions for the qualitative perception change remained unclear because the change could result from interactions at higher processing levels as well. In our electroencephalogram experiment, we combined the McGurk effect with mismatch negativity (MMN), a response that is elicited in the auditory cortex at a latency of 100-250 msec by any above-threshold change in a sequence of repetitive sounds. An "odd-ball" sequence of acoustic stimuli consisting of frequent /va/ syllables (standards) and infrequent /ba/ syllables (deviants) was presented to 11 participants. Deviant stimuli in the unisensory acoustic stimulus sequence elicited a typical MMN, reflecting discrimination of acoustic features in the auditory cortex. When the acoustic stimuli were dubbed onto a video of a mouth constantly articulating /va/, the deviant acoustic /ba/ was heard as /va/ due to the McGurk effect and was indistinguishable from the standards. Importantly, such deviants did not elicit MMN, indicating that the auditory cortex failed to discriminate between the acoustic stimuli. Our findings show that visual stream can qualitatively change the auditory percept at the auditory cortex level, profoundly influencing the auditory cortex mechanisms underlying early sound discrimination.

  7. 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.

  8. Physical connections between different SSVEP neural networks

    PubMed Central

    Wu, Zhenghua

    2016-01-01

    This work investigates the mechanism of the Steady-State Visual Evoked Potential (SSVEP). One theory suggests that different SSVEP neural networks exist whose strongest response are located in different frequency bands. This theory is based on the fact that there are similar SSVEP frequency-amplitude response curves in these bands. Previous studies that employed simultaneous stimuli of different frequencies illustrated that the distribution of these networks were similar, but did not discuss the physical connection between them. By comparing the SSVEP power and distribution under a single-eye stimulus and a simultaneous, dual-eye stimulus, this work demonstrates that the distributions of different SSVEP neural networks are similar to each other and that there should be physical overlapping between them. According to the band-pass filter theory in a signal transferring channel, which we propose in this work for the first time, there are different amounts of neurons that are involved under repetitive stimuli of different frequencies and that the response intensity of each neuron is similar to each other so that the total response (i.e., the SSVEP) that is observed from the scalp is different. PMID:26952961

  9. Representational Distance Learning for Deep Neural Networks.

    PubMed

    McClure, Patrick; Kriegeskorte, Nikolaus

    2016-01-01

    Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

  10. Representational Distance Learning for Deep Neural Networks

    PubMed Central

    McClure, Patrick; Kriegeskorte, Nikolaus

    2016-01-01

    Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains. PMID:28082889

  11. Neural network for solving convex quadratic bilevel programming problems.

    PubMed

    He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie

    2014-03-01

    In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network.

  12. Developing a Neural Network to Act as a Noise Filter

    DTIC Science & Technology

    1992-10-02

    This study uses the neural network simulator called NETS to determine if neural networks could perform a non-linear filtering operation to remove...noise from two-dimensional (2-D) data and produce a noise-free image. Application is geared toward the development and performance of neural network filters...including the development of an optional neural network architecture and the use of-criteria in determining how accurate the net filtered noise-to produce a noise-free image.

  13. 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.

  14. Neural network models: Insights and prescriptions from practical applications

    SciTech Connect

    Samad, T.

    1995-12-31

    Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.

  15. Neural network and its application to CT imaging

    SciTech Connect

    Nikravesh, M.; Kovscek, A.R.; Patzek, T.W.

    1997-02-01

    We present an integrated approach to imaging the progress of air displacement by spontaneous imbibition of oil into sandstone. We combine Computerized Tomography (CT) scanning and neural network image processing. The main aspects of our approach are (I) visualization of the distribution of oil and air saturation by CT, (II) interpretation of CT scans using neural networks, and (III) reconstruction of 3-D images of oil saturation from the CT scans with a neural network model. Excellent agreement between the actual images and the neural network predictions is found.

  16. Neural networks and their application to nuclear power plant diagnosis

    SciTech Connect

    Reifman, J.

    1997-10-01

    The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed.

  17. Using neural networks for dynamic light scattering time series processing

    NASA Astrophysics Data System (ADS)

    Chicea, Dan

    2017-04-01

    A basic experiment to record dynamic light scattering (DLS) time series was assembled using basic components. The DLS time series processing using the Lorentzian function fit was considered as reference. A Neural Network was designed and trained using simulated frequency spectra for spherical particles in the range 0–350 nm, assumed to be scattering centers, and the neural network design and training procedure are described in detail. The neural network output accuracy was tested both on simulated and on experimental time series. The match with the DLS results, considered as reference, was good serving as a proof of concept for using neural networks in fast DLS time series processing.

  18. Classification of asteroid spectra using a neural network

    NASA Technical Reports Server (NTRS)

    Howell, E. S.; Merenyi, E.; Lebofsky, L. A.

    1994-01-01

    The 52-color asteroid survey (Bell et al., 1988) together with the 8-color asteroid survey (Zellner et al., 1985) provide a data set of asteroid spectra spanning 0.3-2.5 micrometers. An artificial neural network clusters these asteroid spectra based on their similarity to each other. We have also trained the neural network with a categorization learning output layer in a supervised mode to associate the established clusters with taxonomic classes. Results of our classification agree with Tholen's classification based on the 8-color data alone. When extending the spectral range using the 52-color survey data, we find that some modification of the Tholen classes is indicated to produce a cleaner, self-consistent set of taxonomic classes. After supervised training using our modified classes, the network correctly classifies both the training examples, and additional spectra into the correct class with an average of 90% accuracy. Our classification supports the separation of the K class from the S class, as suggested by Bell et al. (1987), based on the near-infrared spectrum. We define two end-member subclasses which seem to have compositional significance within the S class: the So class, which is olivine-rich and red, and the Sp class, which is pyroxene-rich and less red. The remaining S-class asteroids have intermediate compositions of both olivine and pyroxene and moderately red continua. The network clustering suggests some additional structure within the E-, M-, and P-class asteroids, even in the absence of albedo information, which is the only discriminant between these in the Tholen classification. New relationships are seen between the C class and related G, B, and F classes. However, in both cases, the number of spectra is too small to interpret or determine the significance of these separations.

  19. Neural mechanisms of coarse-to-fine discrimination in the visual cortex.

    PubMed

    Purushothaman, Gopathy; Chen, Xin; Yampolsky, Dmitry; Casagrande, Vivien A

    2014-12-01

    Vision is a dynamic process that refines the spatial scale of analysis over time, as evidenced by a progressive improvement in the ability to detect and discriminate finer details. To understand coarse-to-fine discrimination, we studied the dynamics of spatial frequency (SF) response using reverse correlation in the primary visual cortex (V1) of the primate. In a majority of V1 cells studied, preferred SF either increased monotonically with time (group 1) or changed nonmonotonically, with an initial increase followed by a decrease (group 2). Monotonic shift in preferred SF occurred with or without an early suppression at low SFs. Late suppression at high SFs always accompanied nonmonotonic SF dynamics. Bayesian analysis showed that SF discrimination performance and best discriminable SF frequencies changed with time in different ways in the two groups of neurons. In group 1 neurons, SF discrimination performance peaked on both left and right flanks of the SF tuning curve at about the same time. In group 2 neurons, peak discrimination occurred on the right flank (high SFs) later than on the left flank (low SFs). Group 2 neurons were also better discriminators of high SFs. We examined the relationship between the time at which SF discrimination performance peaked on either flank of the SF tuning curve and the corresponding best discriminable SFs in both neuronal groups. This analysis showed that the population best discriminable SF increased with time in V1. These results suggest neural mechanisms for coarse-to-fine discrimination behavior and that this process originates in V1 or earlier.

  20. Neural mechanisms of coarse-to-fine discrimination in the visual cortex

    PubMed Central

    Purushothaman, Gopathy; Chen, Xin; Yampolsky, Dmitry

    2014-01-01

    Vision is a dynamic process that refines the spatial scale of analysis over time, as evidenced by a progressive improvement in the ability to detect and discriminate finer details. To understand coarse-to-fine discrimination, we studied the dynamics of spatial frequency (SF) response using reverse correlation in the primary visual cortex (V1) of the primate. In a majority of V1 cells studied, preferred SF either increased monotonically with time (group 1) or changed nonmonotonically, with an initial increase followed by a decrease (group 2). Monotonic shift in preferred SF occurred with or without an early suppression at low SFs. Late suppression at high SFs always accompanied nonmonotonic SF dynamics. Bayesian analysis showed that SF discrimination performance and best discriminable SF frequencies changed with time in different ways in the two groups of neurons. In group 1 neurons, SF discrimination performance peaked on both left and right flanks of the SF tuning curve at about the same time. In group 2 neurons, peak discrimination occurred on the right flank (high SFs) later than on the left flank (low SFs). Group 2 neurons were also better discriminators of high SFs. We examined the relationship between the time at which SF discrimination performance peaked on either flank of the SF tuning curve and the corresponding best discriminable SFs in both neuronal groups. This analysis showed that the population best discriminable SF increased with time in V1. These results suggest neural mechanisms for coarse-to-fine discrimination behavior and that this process originates in V1 or earlier. PMID:25210162

  1. Parameterizing Stellar Spectra Using Deep Neural Networks

    NASA Astrophysics Data System (ADS)

    Li, Xiang-Ru; Pan, Ru-Yang; Duan, Fu-Qing

    2017-03-01

    Large-scale sky surveys are observing massive amounts of stellar spectra. The large number of stellar spectra makes it necessary to automatically parameterize spectral data, which in turn helps in statistically exploring properties related to the atmospheric parameters. This work focuses on designing an automatic scheme to estimate effective temperature ({T}{eff}), surface gravity ({log}g) and metallicity [Fe/H] from stellar spectra. A scheme based on three deep neural networks (DNNs) is proposed. This scheme consists of the following three procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural networks; second, the DNN is fine-tuned using a gradient descent scheme; third, three atmospheric parameters {T}{eff}, {log}g and [Fe/H] are estimated using the computed DNNs. The constructed DNN is a neural network with six layers (one input layer, one output layer and four hidden layers), for which the number of nodes in the six layers are 3821, 1000, 500, 100, 30 and 1, respectively. This proposed scheme was tested on both real spectra and theoretical spectra from Kurucz’s new opacity distribution function models. Test errors are measured with mean absolute errors (MAEs). The errors on real spectra from the Sloan Digital Sky Survey (SDSS) are 0.1477, 0.0048 and 0.1129 dex for {log}g, {log}{T}{eff} and [Fe/H] (64.85 K for {T}{eff}), respectively. Regarding theoretical spectra from Kurucz’s new opacity distribution function models, the MAE of the test errors are 0.0182, 0.0011 and 0.0112 dex for {log}g, {log}{T}{eff} and [Fe/H] (14.90 K for {T}{eff}), respectively.

  2. Deep Recurrent Neural Networks for Supernovae Classification

    NASA Astrophysics Data System (ADS)

    Charnock, Tom; Moss, Adam

    2017-03-01

    We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to the network, but since the inputs are agnostic, additional data such as host galaxy information can also be included. Using the Supernovae Photometric Classification Challenge (SPCC) data, we find that deep networks are capable of learning about light curves, however the performance of the network is highly sensitive to the amount of training data. For a training size of 50% of the representational SPCC data set (around 104 supernovae) we obtain a type-Ia versus non-type-Ia classification accuracy of 94.7%, an area under the Receiver Operating Characteristic curve AUC of 0.986 and an SPCC figure-of-merit F 1 = 0.64. When using only the data for the early-epoch challenge defined by the SPCC, we achieve a classification accuracy of 93.1%, AUC of 0.977, and F 1 = 0.58, results almost as good as with the whole light curve. By employing bidirectional neural networks, we can acquire impressive classification results between supernovae types I, II and III at an accuracy of 90.4% and AUC of 0.974. We also apply a pre-trained model to obtain classification probabilities as a function of time and show that it can give early indications of supernovae type. Our method is competitive with existing algorithms and has applications for future large-scale photometric surveys.

  3. 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.

  4. Neural Network Architectures for General Image Recognition.

    DTIC Science & Technology

    1992-07-21

    Design Procedure 88 A.5 Examples 92 A.6 Scaling Laws for Cooperative-Competitive Neural Networks 98 A.7 Discussion 100 REFERENCES 101 viii LIST OF...control law for vertical pull-in of the window. The two V2 outputs, NORTH and SOUTH, are subtracted and the window is moved if the thresholds are exceeded...An input pattern, shown in cross-hatching, is irapressed on M neurons. Each input neuron is connected to N hidden neutons and a single output neuron

  5. 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

  6. Solving inversion problems with neural networks

    NASA Technical Reports Server (NTRS)

    Kamgar-Parsi, Behzad; Gualtieri, J. A.

    1990-01-01

    A class of inverse problems in remote sensing can be characterized by Q = F(x), where F is a nonlinear and noninvertible (or hard to invert) operator, and the objective is to infer the unknowns, x, from the observed quantities, Q. Since the number of observations is usually greater than the number of unknowns, these problems are formulated as optimization problems, which can be solved by a variety of techniques. The feasibility of neural networks for solving such problems is presently investigated. As an example, the problem of finding the atmospheric ozone profile from measured ultraviolet radiances is studied.

  7. Finite time stabilization of delayed neural networks.

    PubMed

    Wang, Leimin; Shen, Yi; Ding, Zhixia

    2015-10-01

    In this paper, the problem of finite time stabilization for a class of delayed neural networks (DNNs) is investigated. The general conditions on the feedback control law are provided to ensure the finite time stabilization of DNNs. Then some specific conditions are derived by designing two different controllers which include the delay-dependent and delay-independent ones. In addition, the upper bound of the settling time for stabilization is estimated. Under fixed control strength, discussions of the extremum of settling time functional are made and a switched controller is designed to optimize the settling time. Finally, numerical simulations are carried out to demonstrate the effectiveness of the obtained results.

  8. Bacterial colony counting by Convolutional Neural Networks.

    PubMed

    Ferrari, Alessandro; Lombardi, Stefano; Signoroni, Alberto

    2015-01-01

    Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.

  9. Zebrafish tracking using convolutional neural networks

    PubMed Central

    XU, Zhiping; Cheng, Xi En

    2017-01-01

    Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable. PMID:28211462

  10. Zebrafish tracking using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Xu, Zhiping; Cheng, Xi En

    2017-02-01

    Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.

  11. 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.

  12. Neural network models for Linear Programming

    SciTech Connect

    Culioli, J.C.; Protopopescu, V.; Britton, C.; Ericson, N. )

    1989-01-01

    The purpose of this paper is to present a neural network that solves the general Linear Programming (LP) problem. In the first part, we recall Hopfield and Tank's circuit for LP and show that although it converges to stable states, it does not, in general, yield admissible solutions. This is due to the penalization treatment of the constraints. In the second part, we propose an approach based on Lagragrange multipliers that converges to primal and dual admissible solutions. We also show that the duality gap (measuring the optimality) can be rendered, in principle, as small as needed. 11 refs.

  13. Phase diagram of spiking neural networks

    PubMed Central

    Seyed-allaei, Hamed

    2015-01-01

    In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters – excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli. PMID:25788885

  14. Temporal evolution of neural activity underlying auditory discrimination of frequency increase and decrease.

    PubMed

    Noguchi, Yasuki; Fujiwara, Mana; Hamano, Saki

    2015-05-01

    Discriminating a direction of frequency change is an important ability of the human auditory system, although temporal dynamics of neural activity underlying this discrimination remains unclear. In the present study, we recorded auditory-evoked potentials when human subjects explicitly judged a direction of a relative frequency change between two successive tones. A comparison of two types of trials with ascending and descending tone pairs revealed that neural activity discriminating a direction of frequency changes appeared as early as the P1 component of auditory-evoked potentials (latency 50 ms). Those differences between the ascending and descending trials were also observed in subsequent electroencephalographic components such as the N1 (100 ms) and P2 (200 ms). Furthermore, amplitudes of the P2 were significantly modulated by behavioral responses (upward/downward judgments) of subjects in the direction discrimination task, while those of the P1 were not. Those results indicate that, while the neural responses encoding a direction of frequency changes can be observed in an early component of electroencephalographic responses (50 ms after the change), the activity associated (correlated) with behavioral judgments evolves over time, being shaped in a later time period (around 200 ms) of the auditory processing.

  15. Neural Networks for Signal Processing and Control

    NASA Astrophysics Data System (ADS)

    Hesselroth, Ted Daniel

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

  16. Bayesian neural networks for internet traffic classification.

    PubMed

    Auld, Tom; Moore, Andrew W; Gull, Stephen F

    2007-01-01

    Internet traffic identification is an important tool for network management. It allows operators to better predict future traffic matrices and demands, security personnel to detect anomalous behavior, and researchers to develop more realistic traffic models. We present here a traffic classifier that can achieve a high accuracy across a range of application types without any source or destination host-address or port information. We use supervised machine learning based on a Bayesian trained neural network. Though our technique uses training data with categories derived from packet content, training and testing were done using features derived from packet streams consisting of one or more packet headers. By providing classification without access to the contents of packets, our technique offers wider application than methods that require full packet/payloads for classification. This is a powerful advantage, using samples of classified traffic to permit the categorization of traffic based only upon commonly available information.

  17. Overcoming catastrophic forgetting in neural networks

    PubMed Central

    Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia

    2017-01-01

    The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. PMID:28292907

  18. A convolutional neural network neutrino event classifier

    DOE PAGES

    Aurisano, A.; Radovic, A.; Rocco, D.; ...

    2016-09-01

    Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology withoutmore » the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.« less

  19. A convolutional neural network neutrino event classifier

    SciTech Connect

    Aurisano, A.; Radovic, A.; Rocco, D.; Himmel, A.; Messier, M. D.; Niner, E.; Pawloski, G.; Psihas, F.; Sousa, A.; Vahle, P.

    2016-09-01

    Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  20. A convolutional neural network neutrino event classifier

    NASA Astrophysics Data System (ADS)

    Aurisano, A.; Radovic, A.; Rocco, D.; Himmel, A.; Messier, M. D.; Niner, E.; Pawloski, G.; Psihas, F.; Sousa, A.; Vahle, P.

    2016-09-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  1. Programmable synaptic chip for electronic neural networks

    NASA Technical Reports Server (NTRS)

    Moopenn, A.; Langenbacher, H.; Thakoor, A. P.; Khanna, S. K.

    1988-01-01

    A binary synaptic matrix chip has been developed for electronic neural networks. The matrix chip contains a programmable 32X32 array of 'long channel' NMOSFET binary connection elements implemented in a 3-micron bulk CMOS process. Since the neurons are kept off-chip, the synaptic chip serves as a 'cascadable' building block for a multi-chip synaptic network as large as 512X512 in size. As an alternative to the programmable NMOSFET (long channel) connection elements, tailored thin film resistors are deposited, in series with FET switches, on some CMOS test chips, to obtain the weak synaptic connections. Although deposition and patterning of the resistors require additional processing steps, they promise substantial savings in silicon area. The performance of synaptic chip in a 32-neuron breadboard system in an associative memory test application is discussed.

  2. Orthogonal patterns in binary neural networks

    NASA Technical Reports Server (NTRS)

    Baram, Yoram

    1988-01-01

    A binary neural network that stores only mutually orthogonal patterns is shown to converge, when probed by any pattern, to a pattern in the memory space, i.e., the space spanned by the stored patterns. The latter are shown to be the only members of the memory space under a certain coding condition, which allows maximum storage of M=(2N) sup 0.5 patterns, where N is the number of neurons. The stored patterns are shown to have basins of attraction of radius N/(2M), within which errors are corrected with probability 1 in a single update cycle. When the probe falls outside these regions, the error correction capability can still be increased to 1 by repeatedly running the network with the same probe.

  3. Feedback neural networks for ARTIST ionogram processing

    NASA Astrophysics Data System (ADS)

    Galkin, Ivan A.; Reinisch, Bodo W.; Ososkov, Gennadii A.; Zaznobina, Elena G.; Neshyba, Steven P.

    1996-09-01

    Modern pattern recognition techniques are applied to achieve high quality automatic processing of Digisonde ionograms. An artificial neural network (ANN) was found to be a promising technique for ionospheric echo tracing. A modified rotor model was tested to construct the Hopfield ANN with the mean field theory updating scheme. Tests of the models against various ionospheric data showed that the modified rotor model gives good results where conventional tracing techniques have difficulties. Use of the ANN made it possible to implement a robust scheme of trace interpretation that considers local trace inclination angles available after ANN completes tracing. The interpretation scheme features a new algorithm for ƒ0F1 identification that estimates an α angle for the trace segments in the vicinity of the critical frequency ƒ0F1. First results from off-line tests suggest the potential of implementing new operational autoscaling software in the worldwide Digisonde network.

  4. Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-01-01

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

  6. [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.

  7. 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.

  8. Altered Synchronizations among Neural Networks in Geriatric Depression.

    PubMed

    Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C

    2015-01-01

    Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.

  9. Programmable tactile pattern presentations operational under MRI to investigate neural mechanisms of tactile shape discrimination.

    PubMed

    Yang, Jiajia; Wu, Jinglong; He, Jiping

    2011-09-30

    We have developed a novel tactile presentation system for assessing and training cognitive function on tactile senses. The device is operational in magnetic resonance imaging (MRI) environment and capable of investigating the underlying neural mechanisms of tactile pattern discrimination. The primary components of the system include a finger movement control unit (FCU), a disk for multiple tactile pattern delivery (DPD), and a force-sensing unit (FSU). An ultrasonic motor rotates the DPD to deliver different tactile patterns for cognitive sensitivity test. We evaluated the operational reliability and the performance of the system in a MRI environment. The results showed that the system performance was not affected by the magnetic field, nor did the system operation interfere with the magnetic field either. The results from the two functional MRI experiments also indicated that the brain activation can be reliably detected with the present system. Furthermore, a tactile pattern discrimination experiment was conducted using the system to investigate cognitive characteristics of shape discrimination under active and passive touch conditions. We found that the mean accuracy of discrimination under active touch was significantly higher than that under passive touch. The high accuracy and magnetic field compatibility of the device suggest that the device provides a powerful means of investigating the neural mechanisms of perception and cognitive function for touch discrimination.

  10. A neural network model of harmonic detection

    NASA Astrophysics Data System (ADS)

    Lewis, Clifford F.

    2003-04-01

    Harmonic detection theories postulate that a virtual pitch is perceived when a sufficient number of harmonics is present. The harmonics need not be consecutive, but higher harmonics contribute less than lower harmonics [J. Raatgever and F. A. Bilsen, in Auditory Physiology and Perception, edited by Y. Cazals, K. Horner, and L. Demany (Pergamon, Oxford, 1992), pp. 215-222 M. K. McBeath and J. F. Wayand, Abstracts of the Psychonom. Soc. 3, 55 (1998)]. A neural network model is presented that has the potential to simulate this operation. Harmonics are first passed through a bank of rounded exponential filters with lateral inhibition. The results are used as inputs for an autoassociator neural network. The model is trained using harmonic data for symphonic musical instruments, in order to test whether it can self-organize by learning associations between co-occurring harmonics. It is shown that the trained model can complete the pattern for missing-fundamental sounds. The Performance of the model in harmonic detection will be compared with experimental results for humans.

  11. Neural network analysis for hazardous waste characterization

    SciTech Connect

    Misra, M.; Pratt, L.Y.; Farris, C.

    1995-12-31

    This paper is a summary of our work in developing a system for interpreting electromagnetic (EM) and magnetic sensor information from the dig face characterization experimental cell at INEL to determine the depth and nature of buried objects. This project contained three primary components: (1) development and evaluation of several geophysical interpolation schemes for correcting missing or noisy data, (2) development and evaluation of several wavelet compression schemes for removing redundancies from the data, and (3) construction of two neural networks that used the results of steps (1) and (2) to determine the depth and nature of buried objects. This work is a proof-of-concept study that demonstrates the feasibility of this approach. The resulting system was able to determine the nature of buried objects correctly 87% of the time and was able to locate a buried object to within an average error of 0.8 feet. These statistics were gathered based on a large test set and so can be considered reliable. Considering the limited nature of this study, these results strongly indicate the feasibility of this approach, and the importance of appropriate preprocessing of neural network input data.

  12. Spatial analysis using unsupervised neural networks

    NASA Astrophysics Data System (ADS)

    Murnion, Shane D.

    1996-11-01

    Site selection case studies are often used in training exercises or demonstrations to illustrate the advantages of using a geographical information system (GIS). A typical site selection case study might answer the question "where should I locate a new convenience store?" Current GIS can solve spatial analysis problems that are well defined efficiently. Unfortunately many "real world" problems are poorly defined, for example combinatorial spatial optimisation problems. In these problems the value of any solution depends on a number of factors, each of which must be changed and tested to generate an optimum solution. The large number of possible combinations that must be examined can render such problems insoluble using conventional analysis techniques. In this paper an example of a combinatorial spatial optimisation problem, which is nonpolynomial complete in nature, is examined. The problem can be defined as finding the optimum location for multiple retail sites, where the chosen retail sites will compete with each other for customers. It is shown that a solution can be determined using a relatively unsophisticated unsupervised Hopfield neural network algorithm. The neural network solution is generated within an efficient time-frame and it is shown that counter-intuitively, the algorithm becomes more efficient as the complexity of the problem increases.

  13. Segmented-memory recurrent neural networks.

    PubMed

    Chen, Jinmiao; Chaudhari, Narendra S

    2009-08-01

    Conventional recurrent neural networks (RNNs) have difficulties in learning long-term dependencies. To tackle this problem, we propose an architecture called segmented-memory recurrent neural network (SMRNN). A symbolic sequence is broken into segments and then presented as inputs to the SMRNN one symbol per cycle. The SMRNN uses separate internal states to store symbol-level context, as well as segment-level context. The symbol-level context is updated for each symbol presented for input. The segment-level context is updated after each segment. The SMRNN is trained using an extended real-time recurrent learning algorithm. We test the performance of SMRNN on the information latching problem, the "two-sequence problem" and the problem of protein secondary structure (PSS) prediction. Our implementation results indicate that SMRNN performs better on long-term dependency problems than conventional RNNs. Besides, we also theoretically analyze how the segmented memory of SMRNN helps learning long-term temporal dependencies and study the impact of the segment length.

  14. Sentence alignment using feed forward neural network.

    PubMed

    Fattah, Mohamed Abdel; Ren, Fuji; Kuroiwa, Shingo

    2006-12-01

    Parallel corpora have become an essential resource for work in multi lingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to align sentences in bilingual parallel corpora based on feed forward neural network classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuate score, and cognate score values. A set of manually prepared training data has been assigned to train the feed forward neural network. Another set of data was used for testing. Using this new approach, we could achieve an error reduction of 60% over length based approach when applied on English-Arabic parallel documents. Moreover this new approach is valid for any language pair and it is quite flexible approach since the feature parameter vector may contain more/less or different features than that we used in our system such as lexical match feature.

  15. Advanced Plasma Diagnostic Analysis using Neural Networks

    NASA Astrophysics Data System (ADS)

    Tritz, Kevin; Reinke, Matt

    2016-10-01

    Machine learning techniques, specifically neural networks (NN), are used with sufficient internal complexity to develop an empirically weighted relationship between a set of filtered X-ray emission measurements and the electron temperature (Te) profile for a specific class of discharges on NSTX. The NN response matrix is used to calculate the Te profile directly from the filtered X-ray diode measurements which extends the electron temperature time response from the 60Hz Thomson Scattering profile measurements to fast timescales (>10kHz) and greatly expands the applicability of Te profile information to fast plasma phenomena, such as ELM dynamics. This process can be improved by providing additional information which helps the neural network refine the relationship between Te and the corresponding X-ray emission. NN supplement limited measurements of a particular quantity using related measurements with higher time or spatial resolution. For example, the radiated power (Prad) determined using resistive foil bolometers is related to similar measurements using AXUV diode arrays through a complex and slowly time-evolving quantum efficiency curve in the VUV spectral region. Results from a NN trained using Alcator C-Mod resistive foil bolometry and AXUV diodes are presented, working towards hybrid Prad measurements with the quantitative accuracy of resistive foil bolometers and with the enhanced temporal and spatial resolution of the unfiltered AXUV diode arrays. Work supported by Department of Energy Grant #: DE-FG02-09ER55012.

  16. 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.

  17. Damage identification with probabilistic neural networks

    SciTech Connect

    Klenke, S.E.; Paez, T.L.

    1995-12-01

    This paper investigates the use of artificial neural networks (ANNs) to identify damage in mechanical systems. Two probabilistic neural networks (PNNs) are developed and used to judge whether or not damage has occurred in a specific mechanical system, based on experimental measurements. The first PNN is a classical type that casts Bayesian decision analysis into an ANN framework, it uses exemplars measured from the undamaged and damaged system to establish whether system response measurements of unknown origin come from the former class (undamaged) or the latter class (damaged). The second PNN establishes the character of the undamaged system in terms of a kernel density estimator of measures of system response; when presented with system response measures of unknown origin, it makes a probabilistic judgment whether or not the data come from the undamaged population. The physical system used to carry out the experiments is an aerospace system component, and the environment used to excite the system is a stationary random vibration. The results of damage identification experiments are presented along with conclusions rating the effectiveness of the approaches.

  18. Neural networks for convex hull computation.

    PubMed

    Leung, Y; Zhang, J S; Xu, Z B

    1997-01-01

    Computing convex hull is one of the central problems in various applications of computational geometry. In this paper, a convex hull computing neural network (CHCNN) is developed to solve the related problems in the N-dimensional spaces. The algorithm is based on a two-layered neural network, topologically similar to ART, with a newly developed adaptive training strategy called excited learning. The CHCNN provides a parallel online and real-time processing of data which, after training, yields two closely related approximations, one from within and one from outside, of the desired convex hull. It is shown that accuracy of the approximate convex hulls obtained is around O[K(-1)(N-1/)], where K is the number of neurons in the output layer of the CHCNN. When K is taken to be sufficiently large, the CHCNN can generate any accurate approximate convex hull. We also show that an upper bound exists such that the CHCNN will yield the precise convex hull when K is larger than or equal to this bound. A series of simulations and applications is provided to demonstrate the feasibility, effectiveness, and high efficiency of the proposed algorithm.

  19. 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.

  20. Learning and coding in biological neural networks

    NASA Astrophysics Data System (ADS)

    Fiete, Ila Rani

    How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and