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检索条件"主题词=Backpropagation Algorithms"

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Comparison of artificial neural network based ECG classifiers using different features types

Comparison of artificial neural network based ECG classifier...

引用

Computers in Cardiology (CinC)

作者： J.P. Marques de Sa A.P. Goncalves F.O. Ferreira C. Abreu-Lima Faculdade de Engenharia Faculdade de Medicina INEB-Institute Engineering Biom Universidade do Porto Portugal

Artificial neural networks (ANN) have been applied for some years in the field of signal classification with the aim of outperforming the traditional classifiers. The authors address the results of a study that comprehended the design and training of ANNs for ECG classification in four classes. Distinct ANNs having as inputs distinct ECG features types were designed and trained with the aim of attaining a reduced and "best" discriminating features set.< >

关键词： Artificial neural networks Electrocardiography Multilayer perceptrons Pattern classification backpropagation algorithms Hospitals Myocardium Data mining Personal communication networks Software packages

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Is VLSI neural learning robust against circuit limitations?

Is VLSI neural learning robust against circuit limitations?

引用

International Joint Conference on Neural Networks (IJCNN)

作者： H.C. Card B.K. Dolenko D.K. McNeill C.R. Schneider R.S. Schneider Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada National Research Council of Canada Institute of Biodiagnostics Winnipeg MAN Canada Niche Technology Inc. Winnipeg MAN Canada

An investigation is made of the tolerance of various in-circuit learning algorithms to component imprecision and other circuit limitations in artificial neural networks. Supervised learning mechanisms including backpropagation and contrastive Hebbian leaning, and unsupervised soft competitive learning are all shown to be tolerant of those levels of arithmetic inaccuracy, noise, nonlinearity, weight decay, and statistical variation from fabrication that the authors have experienced in 1.2 /spl mu/m analog CMOS circuits employing Gilbert multipliers as the primary computational element. These learning circuits also function properly in the presence of offset errors in analog multipliers and adders, provided that the computed weight updates are constrained by the circuitry to be made only when they exceed certain minimum or threshold values. These results are also relevant for compact (low bit rate) digital implementations.< >

关键词： Very large scale integration Robustness Analog computers backpropagation algorithms Artificial neural networks Supervised learning Arithmetic Circuit noise Noise level Fabrication

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Adaptive packet equalization for indoor radio channel in personal communication services

Adaptive packet equalization for indoor radio channel in per...

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IEEE ComSoc International Workshop on Multimedia Communications

作者： Po-Rong Chang Bor-Chin Wang Chih-Chuang Chang Bao-Fuh Yeh Department of Communication Engineering National Chiao Tung University Hsinchu Taiwan National Chiao Tung University Hsinchu TW

The paper investigates the application of the multilayer perceptron structure to the packet wise adaptive decision feedback equalization of a M-ary QAM signal through a TDMA indoor radio channel in the presence of intersymbol interference (ISI) and additive Gaussian noise. Since the multilayer neural networks are capable of producing complex decision regions with arbitrarily nonlinear boundaries, this would greatly improve the performance of conventional decision feedback equalizer (DFE), where the decision boundaries of conventional DFE's are linear. Nevertheless, the applications of the traditional multilayer neural networks have been limited to real valued signals. To tackle this difficulty, a neural based DFE is proposed to deal with the complex QAM signal over the complex valued fading multipath radio channel without performing time consuming complex valued backpropagation training algorithms, while maintaining almost the same computational complexity as the original real valued training algorithm. Moreover, this neural based DFE trained by packet wise backpropagation algorithm would approach an ideal equalizer after receiving a sufficient number of packets. Results show that the neural based DFE provides a superior bit error rate performance relative to the conventional mean square DFE, especially in poor signal to noise ratio conditions.

关键词： Adaptive equalizers Indoor radio communication Decision feedback equalizers Multi-layer neural network backpropagation algorithms Quadrature amplitude modulation Intersymbol interference Neural networks Multilayer perceptrons Time division multiple access

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Modifying training algorithms for improved fault tolerance

Modifying training algorithms for improved fault tolerance

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ching-Tai Chiu K. Mehrotra C.K. Mohan S. Ranka School of Computer and Information Science Syracuse University NY

This paper presents three approaches to improve fault tolerance of neural networks. In two approaches, the traditional backpropagation training algorithm is itself modified so that the trained net works have improved fault tolerance; we achieve better results than others who had also explored this possibility. Our first method is to coerce weights to have low magnitudes, during the training process, since fault tolerance is degraded by the use of high magnitude weights; at the same time, additional hidden nodes are added dynamically to the network to ensure that desired performance can be reached. Our second method is to add artificial faults to various components (nodes and links) of a network during training. This leads to the development of networks that perform well even when faults occur in the network. The third method repeatedly eliminates nodes of least sensitivity, then "splits" the most sensitive nodes and retrains the system. This generally results in the best performance, although it requires a small amount of additional retraining after a network is built. Experimental results have shown that these methods can obtain better robustness than backpropagation training,and compare favorably with other approaches.< >

关键词： Fault tolerance Neural networks backpropagation algorithms Robustness Computer networks Information science Degradation Neural network hardware Feedforward neural networks Mean square error methods

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Hardware efficient learning on a 3-D optoelectronic neural system

Hardware efficient learning on a 3-D optoelectronic neural s...

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International Joint Conference on Neural Networks (IJCNN)

作者： A.V. Krishnamoorthy S.A. Brodsky C.C. Guest G.C. Marsden M. Blume G. Yayla J. Merckle S.C. Esener Department of Electrical & Computer Engineering University of California San Diego La Jolla CA USA

Discusses the dual-scale topology optoelectronic processor (D-STOP) neural network, a scalable, optically interconnected neural network architecture. The authors present the tandem D-STOP system, which provides the connectivity needed for building fully-parallel neural networks with generic gradient-descent learning rules. The authors review the content addressable network (CAN) learning algorithm, a discrete learning algorithm that provides accelerated learning with reduced hardware requirements. The authors then show how the CAN algorithm can be effectively mapped onto D-STOP, and they investigate associated optoelectronic hardware tradeoffs.< >

关键词： Hardware Neurons Optical transmitters Optical interconnections Neural networks Neurotransmitters Optical network units Integrated circuit interconnections Power system interconnection backpropagation algorithms

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Training hard-limiting neurons using back-propagation algorithm by updating steepness factors

Training hard-limiting neurons using back-propagation algori...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiangui Yu N.K. Loh W.C. Miller Department of Electrical Engineering University of Windsor Windsor ONT Canada Center for Robotics and Advanced Automation Dodge Hall of Engineering Oakland University Rochester MI USA

This paper presents one kind of modified backpropagation algorithm for training the multilayer feedforward neural networks with hard-limiting neurons. Adaptive steepness factors in the analog sigmoidal neuron activation functions are updated in the training process. With the decrease of the sum-square error, these steepness factors are varied from a small positive value to infinite. It makes the sigmoidal neuron transferred to hard-limiting one after the training process complete. Thus, a multilayer feedforward neural network can be trained with the resultant architecture is only composed of hard-limiting neurons. The learning algorithm is similar to the conventional backpropagation algorithm, only the derivatives of the hidden neural activation functions are modified according to the proposed idea. Extensive numerical simulations are presented to show the feasibility of the proposed algorithm. In addition, the numerical properties of the proposed algorithm are also discussed in detail. Comparisons of the proposed algorithm with algorithms are given, and some useful conclusions are drawn.< >

关键词： Neurons backpropagation algorithms Multi-layer neural network Neural networks Feedforward neural networks Very large scale integration Robotics and automation Multilayer perceptrons Numerical simulation Artificial neural networks

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Learning without local minima

Learning without local minima

引用

International Joint Conference on Neural Networks (IJCNN)

作者： J. Barhen N. Toomarian A. Fijany Jet Propulsion Laboratory USA Applied Physics Department California Institute of Technology Pasadena CA USA California Institute of Technology Pasadena CA USA

A computationally efficient methodology for overcoming local minima in nonlinear neural network learning is presented. This methodology is based on the newly discovered TRUST global optimization paradigm. Enhancements to the backpropagation schema in feedforward multilayer architectures, and to adjoint-operator learning in recurrent networks are discussed. Extensions to TRUST now formally guarantee reaching a global minimum in the multidimensional case. Results for a standard benchmark are included, to illustrate the theoretical developments.< >

关键词： Computer architecture Neural networks Optimization methods Minimization methods Nonlinear dynamical systems Tunneling Computer networks Multi-layer neural network backpropagation algorithms Computational efficiency

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Projection-based methods for stepsize adaptation and their application to the training of feedforward artificial neural networks

Projection-based methods for stepsize adaptation and their a...

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International Joint Conference on Neural Networks (IJCNN)

作者： C.W. Codrington M. Mohandes School of Electrical Engineering Purdue University West Lafayette IN USA

Develops several adaptive step-size rules for gradient descent based on projecting weight and gradient vectors onto a set of unit vectors; each unit vector induces a one dimensional optimization problem which is solved by minimizing a fitted quadratic. A sum of squares criterion is then used to find the stepsize which which best fits the solution to each one dimensional optimization. The resulting stepsize rules are applied to train neural networks on parity problems of various sizes.< >

关键词： Stability Neural networks Size measurement Time measurement Artificial neural networks backpropagation algorithms Feedforward neural networks Error correction Convergence Interpolation

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Localization of epileptogenic foci using artificial neural networks

Localization of epileptogenic foci using artificial neural n...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： I.E. Ouaiss A.P. Dhawan M.D. Privitera Department of Electrical and Computer Engineering University of Cincinnati OH USA Department of Neurology University of Cincinnati OH USA

Current neuropsychological tests based clinical methods are often difficult to interpret for localizing seizure foci in epilepsy patients. The purpose of this study is to predict with a high degree of certainty the location of the epileptogenic foci in epilepsy patients. First, neuropsychological tests data containing information thought to be relevant to the decision making process was extracted from patients' files. Next, the collected data was normalized and based on statistical analysis techniques. A set of best features was selected. These selected features were then analyzed using different classification techniques. The performance of each classifier was compared through the receiver operating characteristic analysis. Results show that the radial basis function classifier yielded the most promising results although other classification techniques produced satisfactory results as well.

关键词： Epilepsy Testing backpropagation algorithms Neural networks Biomedical imaging Clustering algorithms Performance analysis Surgery Medical treatment Radial basis function networks

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Real-time neurocontrol of a pendulum system

Real-time neurocontrol of a pendulum system

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Wei-Chung Yang M.T. Hagan Castro Valley CA USA Oklahoma State University Stillwater IL USA

This research investigates the use of neural networks for system identification and control of nonlinear dynamic systems. Supervised learning algorithms are used to train both feedforward and recurrent neural networks... 详细信息

关键词： Real time systems Neurocontrollers Control systems Neural networks Signal processing algorithms Nonhomogeneous media Recurrent neural networks System testing backpropagation algorithms System identification

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