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检索条件"主题词=Backpropagation Algorithms"

共 1893 条记录，以下是251-260 订阅

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A margin maximization training algorithm for BP network

A margin maximization training algorithm for BP network

引用

4th International Symposium on Neural Networks (ISNN 2007)

作者： Wang, Kai Wang, Qingren Nankai Univ Inst Machine Intelligence Tianjin 300071 Peoples R China

ISBN: (纸本)9783540723929

Generalization problem is a key problem in NN society, which can be grouped into two classes: the generalization problem with unlimited size of training sample and that with limited size of training sample. The generalization problem with limited size of training sample is considered in this paper. Similar to margin maximization criterion in SVM, we propose a margin maximization training algorithm for BP network to further improve the generalization ability of BP network. Experimental results show that the margin maximization training algorithm proposed in this paper does improve the performance of BP network, and shows a comparable performance with SVM.

关键词： backpropagation algorithms

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Performance Evaluation of Two Distributed backpropagation Implementations

Performance Evaluation of Two Distributed BackPropagation Im...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Sorin Babii Vladimir Cretu Emil M. Petriu Department of Computers Politehnica University of Timişoara Timisoara Romania School of Information Technology and Engineering University of Ottawa Ottawa ONT Canada

This article presents the results of some experiments in parallelizing the training phase of a feed-forward, artificial neural network. More specifically, we develop and analyze a parallelization strategy of the widely used neural net learning algorithm called back-propagation. We describe two strategies for parallelizing the back-propagation algorithm. We implemented these algorithms on several LANs, permitting us to evaluate and analyze their performances based on the results of actual runs. We were interested on the qualitative aspect of the analysis, in order to achieve a fair understanding of the factors determining the behavior of this parallel algorithms. We were interested in discovering and dealing with some of the specific circumstances that have to be considered when a parallelized neural net learning algorithm is to be implemented on a set of workstations in a LAN. Part of our purpose is to investigate whether it is possible to exploit the computational resources of such a set of workstations.

关键词： Algorithm design and analysis Artificial neural networks backpropagation algorithms Workstations Feedforward systems Performance analysis Performance evaluation Parallel algorithms Neural networks Local area networks

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Combination of adaptive signal processing and neural classification using an extended backpropagation algorithm

Combination of adaptive signal processing and neural classif...

引用

IEEE Workshop on Neural Networks for Signal Processing

作者： A. Doering H. Witte Institute of Medical Statistics Computer Science and Documentation Friedrich-Schiller-Universitat Jena Jena Thuringen Germany

Beside the use of purely neural systems, the combination of preprocessing units and neural classifiers has been used for a variety of signal segmentation and classification tasks. Whereas this approach reduces the input dimensionality as well as the complexity of the classification problem, its performance crucially depends on a proper preprocessing scheme, i.e., feature extraction. In this contribution, adaptive preprocessing units (frequency-selective quadrature filters) are proposed that can be adjusted in order to provide optimal features. The mean frequencies of the filters are tuned to minimize the classification error. Both FIR- and IIR-based filters are introduced and compared with respect to their convergence properties and the classification results. Results for the solution of an EEG segmentation task using the combined system are given.

关键词： Adaptive signal processing Frequency Neural networks Signal processing algorithms Data preprocessing Adaptive filters Electroencephalography Signal processing Robust stability backpropagation algorithms

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backpropagation algorithm for logic oriented neural networks

Backpropagation algorithm for logic oriented neural networks

引用

International Joint Conference on Neural Networks (IJCNN)

作者： T. Kamio S. Tanaka M. Morisue Department of Information Machines and Interfaces Faculty of Information Sciences Hiroshima City University Hiroshima Japan

Multilayer feedforward neural network (MFNN) trained by the backpropagation (BP) algorithm is one of the most significant models in artificial neural networks. Although they have been implemented as analog, mixed analog-digital and fully digital VLSI circuits, it is still difficult to realize their hardware implementation with BP learning function. This paper describes the BP algorithm for the logic oriented neural network (LOGO-NN) which we have proposed as a kind of MFNN with quantized weights and multilevel threshold neurons. Since both weights and neuron outputs are quantized to integer values in LOGO-NNs, it is expected that LOGO-NNs with BP learning can be more effectively implemented than the common MFNNs. Finally, it is shown by simulations that the proposed BP algorithm has good performance for LOGO-NNs.

关键词： backpropagation algorithms Logic Neural networks Artificial neural networks Multi-layer neural network Neurons Feedforward neural networks Analog-digital conversion Very large scale integration Circuits

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Technique to design MLP networks in CMOS technology with adjustment of the backpropagation algorithm

Technique to design MLP networks in CMOS technology with adj...

引用

Brazilian Symposium on Neural Networks

作者： Fd.A. Pereira J.J.F. Cerqueira Departamento de Engenharia Elétrica da UFBA Salvador BA Brazil

The implementation of CMOS multipliers have been based on the use of either analog circuits for continuous time signal processing or switched current techniques for discrete time processing, once current mode topologies have become a trend for IC designers in low voltage applications. In this work we perform an analysis of some techniques for implementing the basic building blocks in a neural network using the IC technology for analog signal processing. Even though we focus on the operation of MOS devices in the strong inversion region deep into saturation, some aspects of the synapses in the weak inversion region are also explained, which has been accomplished through the use of classical asymptotical models for the drain current.

关键词： CMOS technology Algorithm design and analysis backpropagation algorithms CMOS analog integrated circuits Analog integrated circuits CMOS process Analog circuits Signal processing algorithms Switching circuits Network topology

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Speed training: improving the rate of backpropagation learning through stochastic sample presentation

Speed training: improving the rate of backpropagation learni...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M.E. Rimer T.L. Andersen T.R. Martinez Computer Science Department Brigham Young University Provo UT USA

ISBN: (纸本)0780370449

Artificial neural networks provide an effective empirical predictive model for pattern classification. However, using complex neural networks to learn very large training sets is often problematic, imposing prohibitive time constraints on the training process. We present four practical methods for dramatically decreasing training time through dynamic stochastic sample presentation, a technique we call speed training. These methods are shown to be robust to retaining generalization accuracy over a diverse collection of real world data sets. In particular, the stochastic presentation with error threshold technique achieves a training speedup of 4278% on a large OCR database with no detectable loss in generalization.

关键词： Stochastic processes Training data Neural networks Testing Robustness backpropagation algorithms Convergence Computer science Predictive models Time factors

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Reducing the herd effect in the backpropagation algorithm

Reducing the herd effect in the backpropagation algorithm

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Poh Lian Choong C.J.S. deSilva Department of Electrical and Electronic EngineeringCentre for Intelligent Information Processing Systems University of Western Australia Nedlands Australia Department of Electrical and Electronic Engineering Centre for Intelligent Information Processing Systems University of Western Australia Nedlands Australia

The backpropagation algorithm is the most popular training algorithm for the multilayer perceptron, despite its slowness. Part of this slowness may be attributed to a phenomenon of the training process that has been called the herd effect. This paper describes a modification of the original backpropagation algorithm that is intended to reduce or eliminate the herd effect and reports on some performance comparisons.

关键词： backpropagation algorithms Helium Artificial neural networks Intelligent systems Information processing Multilayer perceptrons Convergence Cows Testing Mean square error methods

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backpropagation Modification in Monte-Carlo Game Tree Search

Backpropagation Modification in Monte-Carlo Game Tree Search

引用

International Symposium on Intelligent Information Technology Application, IITA

作者： Fan Xie Zhiqing Liu Jiu-Ding Computer Go Research Institute Beijing University of Posts and Telecommunications Beijing China

The Algorithm UCT, proposed by Kocsys et al, which apply multi-armed bandit problem into the tree-structured search space, achieves some remarkable success in some challenging fields. For UCT algorithm, Monte-Carlo simulations are performed with the guidance of UCB1 formula, which are averaged to evaluate a specified action. We observe that, as more simulations are performed, later ones usually lead to more accurate results, partly because the level of the search used in the later simulation is deeper and partly because more results are available to direct subsequent simulations. This paper presents a new method to improve the performance of UCT algorithm by increasing the feedback value of the later simulations. And the experimental results in the classical game Go show that our approach increases the performance of Monte-Carlo simulations significantly when exponential models are used.

关键词： Feedback Application software Computational modeling Telecommunication computing Machine learning Distributed computing Financial advantage program Information technology backpropagation algorithms Performance evaluation

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Exponentiated backpropagation algorithm for multilayer feedforward neural networks

Exponentiated backpropagation algorithm for multilayer feedf...

引用

International Conference on Neural Information Processing

作者： N. Srinivasan V. Ravichandran K.L. Chan J.R. Vidhya S. Ramakirishnan S.M. Krishnan Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur India Department of Electronics and Communication Engineering Biomedical Engineering Research Centre School of Electrical and Electronics Engineering Nanyang Technological University Singapore Department of Mathematics and Computer Applications Sri Venkateswara College of Engineering Sriperumbudur India Department of Mathematics and Computer Applications Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (纸本)9810475241

The gradient descent backpropagation learning algorithm is based on minimizing the mean square error. An alternate approach to gradient descent is the exponentiated gradient descent algorithm which minimizes the relative entropy. Exponentiated gradient descent applied to backpropagation is proposed for a multilayer feedforward neural network. The learning rules for changing weights in the output layer as well the hidden layer neurons in the network are developed. Simulations were performed to explore the convergence and learning of the backpropagation algorithm with exponentiated gradient descent. Accuracy obtained with exponentiated gradient descent back propagation was comparable to the gradient descent back propagation while convergence was faster. The results show that exponentiated gradient descent can be extended to a multilayer feedforward neural network and used in pattern classification applications.

关键词： Multi-layer neural network backpropagation algorithms Neural networks Feedforward neural networks Neurons Biomedical engineering Feeds Mean square error methods Convergence Pattern classification

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backpropagation learning algorithm for nondestructive testing by thermal imager [aerospace materials]

Backpropagation learning algorithm for nondestructive testin...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： F. Kojima H. Kawaguchi Mechanical Engineering Department Osaka Institute of Technology Osaka Japan

An artificial neural network is applied to nondestructive inspections using thermal imager. The use of an artificial neural network is presented for classifying test data as corresponding to bonded and disbonded regions in sample materials. The backpropagation learning for a multi-layer feedforward neural network is applied to this classification. The trust region method is adopted to the backpropagation learning problem. Results of numerical tests are summarized.

关键词： backpropagation algorithms Nondestructive testing Aerospace materials Artificial neural networks Inspection Aerospace testing Materials testing Bonding Neural networks Multi-layer neural network

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