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ATTENUATION AND DETECTOR RESPONSE COMPENSATIONS USED WITH GIBBS PRIOR DISTRIBUTIONS FOR MAXIMUM A POSTERIORI SPECT RECONSTRUCTION

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 1992年第5期39卷 1454-1459页

作者： LALUSH, DS TSUI, BMW UNIV N CAROLINA DEPT BIOMED ENGN CHAPEL HILL NC 27514 USA UNIV N CAROLINA DEPT MATH CHAPEL HILL NC 27514 USA

We study the relationship between the choice of parameters for a generalized Gibbs prior for the MAP-EM (Maximum A Posteriori, Expectation Maximization) algorithm, and the model of the projection/backprojection process used in SPECT reconstruction. A realistic phantom, derived from an X-ray CT study and average Tl-201 uptake distributions in patients, was used in the investigation. Simulated projection data including nonuniform attenuation, detector response, scatter, and Poisson noise were generated. From this data set, reconstructions were created using a MAP-EM technique with a generalized Gibbs prior, which is designed to smooth noise with minimal smoothing of edge information. The Gibbs prior has three adjustable parameters: one which determines the overall weight placed on the prior in the reconstruction process, and two others which affect the relative smoothing of noise and edges in the reconstructed image estimates. Reconstructions were performed over several different values of the prior parameters for three projector/backprojector models: one with no compensations at all, one incorporating only nonuniform attenuation compensation, and one incorporating both nonuniform attenuation and detector response compensations. Analyzing several measures of image quality in a region-of-interest surrounding the myocardium, we conclude that, for each projection model, there is an optimal value of the weighting parameter which decreases as the projection process is modeled more accurately. We conclude also that the use of the Gibbs prior together with nonuniform attenuation and detector response compensations offers improved quantitative accuracy over Maximum Likelihood EM reconstruction with the same compensations.

关键词： Attenuation Detectors Image reconstruction backpropagation algorithms Single photon emission computed tomography Smoothing methods Image edge detection Image analysis Image quality Myocardium

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Genetics-based machine learning for the assessment of certain neuromuscular disorders

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1996年第2期7卷 427-439页

作者： Pattichis, CS Schizas, CN Department of Computer Science University of Cyprus Nicosia Cyprus

Clinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks (ANN's) in classifying EMG data trained with backpropagation or Kohonen's self-organizing feature maps algorithm has recently been demonstrated. The objective of this study is to investigate how genetics-based machine learning (GBML) can be applied for diagnosing certain neuromuscular disorders based on EMG data. The effect of GBML control parameters on diagnostic performance is also examined. A hybrid diagnostic system Is introduced that combines both neural network and GBML models. Such a hybrid system provides the end-user with a robust and reliable system, as its diagnostic performance relies on more than one learning principle. In the clinical EMG laboratory, 680 motor unit action potentials (MUAP's) were collected from 12 normal, 11 motor neuron disease, and 11 myopathy subjects. Eight subjects from each group formed the training set, and the other 10 subjects formed the evaluation set. Each subject was described by a 14-element feature vector consisting of the mean and the standard deviation of each of the following MUAP parameters: duration, spike duration, amplitude, area, spike area, phases, and turns. More than a thousand GBML models were developed by varying the following parameters: message length size (49, 74), number of classifiers (100, 150, 200, 250, 300, 500), lifetax (0.000, 0.002, 0.005, 0.010), period of genetic algorithm (GA) introduced, which is expressed in iterations, showing how often the classifier system calls the GA (50, 100, 200, 500), crossover probability (0.5, 1.0), and mutation probability (0.00, 0.01, 0.02). A total of 28 models were selected that achieved a diagnostic yield better than 95% and 70% for the training and evaluation sets, respectively. This criterion, suggested by two expert neurophysiologists, has formed the bases for classifying a GBML model as ''successful'' and worthy

关键词： Machine learning Neuromuscular Electromyography Muscles Neural networks Diseases Senior members Artificial neural networks backpropagation algorithms Robustness

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A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2001年第9期39卷 1969-1977页

作者： Muchoney, D Williamson, J Boston Univ Dept Geog Boston MA 02215 USA Boston Univ Dept Cognit & Neural Syst Boston MA 02215 USA

Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the predominant neural network architecture has been the feedforward multilayer perceptron known as backpropagation, Adaptive resonance theory (ART) neural networks offer advantages to the classification of optical remote sensing data for vegetation and land cover mapping. A significant advantage is that it does not require prior specification of the neural net structure, creating as many internal nodes as are needed to represent the calibration (training) data. The Gaussian ARTMAP classification algorithm bases the probability that input training samples belong to specific classes on the parameters of its Gaussian distributions: the means, standard deviations, and a priori probabilities. The performance of the Gaussian ARTMAP classification algorithm in terms of classification accuracy using independent validation data indicated was over 70% accurate when applied to an annual series of monthly 1-km advanced very high resolution radiometer (AVHRR) satellite normalized difference vegetation index (NDVI) data. The accuracies were comparable to those of fuzzy ARTMAP and a univariate decision tree, and significantly higher than a Bayesian classification algorithm. Algorithm testing is based on calibration and validation data developed and applied to Central America to map the International Geosphere-Biosphere Programme (IGBP) land cover classification system. Thus, it provides a realistic test of the algorithms for operational classification of a regional remote sensing and site dataset.

关键词： Resonance Neural networks Classification algorithms Vegetation mapping Bayesian methods Calibration backpropagation algorithms Multi-layer neural network Feedforward neural networks Multilayer perceptrons

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3-D WAFER-SCALE ARCHITECTURES FOR NEURAL-NETWORK COMPUTING

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IEEE TRANSACTIONS ON COMPONENTS HYBRIDS AND MANUFACTURING TECHNOLOGY 1993年第7期16卷 646-655页

作者： CAMPBELL, ML TOBORG, ST HUGHES RES LABS MALIBUCA 90265

We introduce a class of massively parallel computer architectures which can be configured to efficiently handle a variety of neural network models. The underlying technology is three-dimensional wafer scale integration (3-D WSI), which provides an ideal medium to construct powerful, compact, and low power hardware tailored for neural network processing. A second generation prototype computer consisting of a 128 x 128 array of processors formed by stacking 8 CMOS wafers is nearing completion. The performance of this prototype is compared with enhanced architectures configured with special wafer types to accelerate neural network operations. The design of these specialized resources emphasizes the synergy between neural processing functions and the 3-D WSI architecture and packaging. Detailed microcode emulations are used to assess the impact of different algorithm/architecture modifications. Neural networks for cooperative vision integration and multilayer backpropagation are mapped onto various 3-D wafer stacks. Estimated performance for the vision integration network is 2.4 billion connections per second. For the backprop network training algorithm, the performance ranges from 1.1 billion connection updates per second (GCUPS) for a near-term 128 x 128 prototype up to 53.4 GCUPS for a future 512 x 512 machine with more extensive neural processing hardware enhancements.

关键词： Computer architecture Neural networks Computer networks Prototypes Neural network hardware backpropagation algorithms Multi-layer neural network Semiconductor device modeling CMOS technology Wafer scale integration

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A FRAMEWORK FOR-IMPROVED TRAINING OF SIGMA-PI NETWORKS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第4期6卷 893-903页

作者： HEYWOOD, M NOAKES, P Department of Electronic Systems Engineering University of Essex Colchester Essex UK

This paper proposes and demonstrates a framework for Sigma-Pi networks such that the combinatorial increase in product terms is avoided. This is achieved by only implementing a subset of the possible product terms (sub-net Sigma-Pi), Application of a dynamic weight pruning algorithm enables redundant weights to be removed and replaced during the learning process, hence permitting access to a larger weight space than employed at network initialization, More than one learning rate is applied to ensure that the inclusion of higher order descriptors does not result in over description of the training set (memorization), The application of such a framework is tested using a problem requiring Significant generalization ability. Performance of the resulting sub-net Sigma-Pi network is compared to that returned by optimal multi-layer perceptrons and general Sigma-Pi solutions.

关键词： Network topology Heuristic algorithms Multilayer perceptrons Neural networks backpropagation algorithms Testing Multi-layer neural network Detectors Performance gain

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The dependence identification neural network construction algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1996年第1期7卷 3-15页

作者： Moody, JO Antsaklis, PJ Department of Electrical Engineering University of Notre Dame Notre Dame IN USA

An algorithm for constructing and training multilayer neural networks, dependence identification, is presented in this paper. Its distinctive features are that i) it transforms the training problem into a set of quadratic optimization problems that are solved by a number of linear equations, ii) it constructs an appropriate network to meet the training specifications, and iii) the resulting network architecture and weights can be further refined with standard training algorithms, like backpropagation, giving a significant speedup in the development time of the neural network and decreasing the amount of trial and error usually associated with network development.

关键词： Neural networks Neurons backpropagation algorithms Iterative algorithms Transforms Equations Standards development Multi-layer neural network Intelligent networks

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NEW NONLEAST-SQUARES NEURAL-NETWORK LEARNING algorithms FOR HYPOTHESIS-TESTING

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第3期6卷 596-609页

作者： PADOS, DA PAPANTONIKAZAKOS, P UNIV VIRGINIA DEPT ELECT ENGNCOMMUN SYST LABCHARLOTTESVILLEVA UNIV OTTAWA OTTAWAONCANADA

Hypothesis testing is a collective name for problems such as classification, detection, and pattern recognition, Neural network solutions to this type of problems would be very desirable, but traditional least square learning algorithms, such as backpropagation, provide no guarantee for success, In this paper we propose two new classes of supervised learning algorithms for feedforward, binary-output neural network structures whose objective is hypothesis testing, Their novel characteristic is the underlying performance criterion, All algorithms are applications of stochastic approximation and are guaranteed to provide optimization with probability one, The first class of algorithms follows the Neyman-Pearson approach and maximizes the probability of detection, subject to a given false alarm constraint. These algorithms produce layer-by-layer optimal Neyman-Pearson designs, The second class of algorithms minimizes the probability of error and leads to layer-by-layer Bayes optimal designs, Deviating from the layer-by-layer optimization assumption, we propose more powerful learning techniques, These techniques unify, in some sense, the already existing algorithms, In fact, the proposed Bayes algorithm and the perceptron learning rule exhibit striking similarities, and the former can be viewed as an improved, generalized version of the latter. Aside from theoretical arguments, the proposed algorithms were implemented and tested on a simulated hypothesis testing problem, backpropagation and Perceptron learning were also included in the comparisons.

关键词： Neural networks Testing backpropagation algorithms Least squares methods Bayesian methods Pattern recognition Algorithm design and analysis Error correction Stochastic processes Least squares approximation

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TRAINING THE HIGHER-ORDER MOMENT NEURAL ARRAY

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1994年第7期42卷 1884-1887页

作者： PORTER, WA LIGADE, S Department of Electrical and Computer Engineering University of Alabama Huntsville Huntsville AL USA

The research summarized herein is concerned with an algorithm which stores information and provides a learning capability. This algorithm is affiliated with a class of neural networks which are referred to as higher order moment neural arrays. The algorithm may be viewed as a generalized training procedure for the affiliated networks.

关键词： Signal processing algorithms Multidimensional signal processing Digital filters Biomedical signal processing Array signal processing backpropagation algorithms Circuits Artificial neural networks Biological system modeling Neural networks

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Fuzzy parameter adaptation in optimization: Some neural net training examples

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IEEE COMPUTATIONAL SCIENCE & ENGINEERING 1996年第1期3卷 57-65页

作者： Arabshahi, P Choi, JJ Marks, RJ Caudell, TP UNIV WASHINGTON SEATTLEWA 98195 BOEING CO COMP SERVSEATTLEWA 98124 UNIV NEW MEXICO ALBUQUERQUENM 87131

Many types of artificial neural networks require training to "learn" the tasks they will be called upon to do, which basically translates into properly setting the weights of the interneuron connections or "synapses" so that the network will give the desired output. Values of certain parameters in the algorithms used to train the networks must be chosen and then adapted or optimized carefully. Error backpropagation training of the multilayer perceptron, for example, requires judicious choice of the step and momentum parameters. The ART network requires choice of a vigilance parameter, while a learning rate must be selected for Kohonen networks. These parameters are typically chosen and adapted by a human "neural smith" using heuristic rules. For example, a smooth error surface in the backpropagation training of a layered perceptron suggests use of a long step, whereas a steep surface indicates a need for smaller steps. The heuristic description of the parameter selection is fuzzy. The terms "smooth," "long," "steep," and "smaller" are each fuzzy linguistic variables that can be quantified into a fuzzy inference engine. A properly designed fuzzy controller, then, can relieve the neural smith of some labor, and get the job done quicker and better. The concept has broader possible applications, beyond neural network training, to parameter selection and optimization generally.

关键词： Fuzzy neural networks Neural networks Fuzzy control Fuzzy sets Fuzzy systems Artificial neural networks backpropagation algorithms Subspace constraints Humans Fuzzy logic

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An ArcGIS Tool for Modeling the Climate Envelope with Feed-Forward ANN

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APPLIED ARTIFICIAL INTELLIGENCE 2015年第3期29卷 233-242页

作者： Bede-Fazekas, Akos Horvath, Levente Trajer, Attila J. Gregorics, Tibor Corvinus Univ Budapest Dept Garden & Open Space Design H-1118 Budapest Hungary Corvinus Univ Budapest Dept Math & Informat H-1118 Budapest Hungary Univ Pannonia Dept Limnol Veszprem Hungary MTA PE Limnoecol Res Grp Veszprem Hungary Eotvos Lorand Univ Dept Software Technol & Methodol Budapest Hungary

This article is about the development and application of an ESRI ArcGIS tool that implements a multilayer, feed-forward artificial neural network (ANN) to study the climate envelopes of species. The supervised learning is achieved by a backpropagation algorithm. Based on the distribution and the grids of the climate (and edaphic data) of the reference and future periods, the tool predicts the future potential distribution of the studied species. The trained network can be saved and loaded. A modeling result based on the distribution of European larch (Larix decidua Mill.) is presented as a case study.

关键词： Modeling Climate Artificial neural networks backpropagation algorithms Larix decidua FEEDFORWARD

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