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检索条件"主题词=Backpropagation Algorithms"

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A weight value initialization method for improving learning performance of the backpropagation algorithm in neural networks

A weight value initialization method for improving learning ...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： H. Shimodaira Nihon MECCS Co. Ltd. Tokyo Japan

In this paper, we propose a new method (the OIVS method) for initializing weight values, which is based on the equations representing the characteristics of the information transformation mechanism of a node. Numerical simulations show that the learning performance of the OIVS method is superior to that of the conventional method. It should be noted that if we use appropriate values of the parameters in the OIVS method, the nonconvergence case can be avoided.< >

关键词： Equations Neural networks Multi-layer neural network backpropagation algorithms Numerical simulation

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A note on backpropagation, projection learning, and feedback in neural systems

A note on backpropagation, projection learning, and feedback...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： K. Weigl I.N.R.I.A. Sophia Antipolis Sophia-Antipolis France

We show instances where parts of algorithms similar to backpropagation respectively projection learning algorithms have been implemented via feedback in neural systems. The corresponding algorithms, with the same or a similar mathematical expression, do not minimize an error in the output space of the network, but rather in the input space of the network, via a comparison between the function to be approximated and the current approximation executed by the network, which is fed back to the input space: We argue that numerous interlayer resp. intracortical feedback connections, e.g. in the visual primary system of mammals, could serve exactly this purpose. We introduce the paradigm with linear operators for illustration purposes, show the extension to nonlinear operators in function space, introduce projection learning, and discuss future work.< >

关键词： Neurofeedback Gabor filters backpropagation algorithms Pixel Neurons Output feedback Vectors Equations Approximation algorithms Helium

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Choosing among several parallel implementations of the backpropagation algorithm

Choosing among several parallel implementations of the backp...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Petrowski Départment Informatique Institut National de Télécommunications Evry France

Three parallel implementations of the backpropagation algorithm are studied. The performance for each of these implementations is estimated for three prototypes of neural architectures chosen in such a way that they are limit cases of a wide range of feedforward architectures. This study, based on performance analysis validated by experiments, follows two other papers already published.< >

关键词： backpropagation algorithms Neurons Parallel processing Computer networks Neural networks Feedforward systems Iterative algorithms Prototypes Performance analysis Pipelines

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Factors influencing the choice of a learning rate for a backpropagation neural network

Factors influencing the choice of a learning rate for a back...

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Roy Command and Control Division Defence Research Establishment Courcelette QUE Canada

Neural networks have been used effectively in a number of applications. Most of these applications have used the backpropagation algorithm as the learning algorithm. One of the major problems with this algorithm is that its convergence time is usually very long since the training set must be presented many times to the network. The learning rate has to be selected very carefully. If the learning rate is too low, the network will take longer to converge. On the other hand, if the learning rate is too high, the network may be unstable and may never converge. Up to now, designers of neural network applications had to find an appropriate learning rate for their systems by trial and error. In this paper, a new method to compute dynamically the optimal learning rate is proposed. By using this method, a range of good static learning rates could be found. Furthermore, some studies have been done on factors influencing learning rates.< >

关键词： Neural networks Convergence Computer networks Equations Command and control systems backpropagation algorithms

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Optimal distribution of patterns in a heterogeneous array of transputers for backpropagation networks

Optimal distribution of patterns in a heterogeneous array of...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Foo Shou King P. Saratchandran N. Sundararajan School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Training set parallelism and network based parallelism are two popular paradigms for parallelising a feedforward (artificial) neural network. Training set parallelism is particularly suited to feedforward neural networks with backpropagation learning where the size of the training set is large in relation to the size of the network. This study analyses how we can optimally distribute the training set on a heterogeneous processor network when the number of patterns in the training set is not an integer multiple of the number of processors. It is shown that optimal allocation of patterns in such cases is a mixed integer programming problem. Using this analysis, it is found that equal distribution of training patterns among a homogeneous array of transputers is not necessarily the optimal way to allocate the patterns to processors even when the training set is an integer multiple of the number of processors.< >

关键词： Intelligent networks backpropagation algorithms Neural networks Network topology Feedforward neural networks Pattern analysis Pipelines Electronic mail Linear programming Parallel machines

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A modified backpropagation algorithm

A modified backpropagation algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： B.K. Verma J.J. Mulawka Politechnika Warszawska Warszawa PL Dept. of Fundamental Electron. Warsaw Univ. of Technol. Poland

A long and uncertain training process is one of the most important problems for a multilayer neural network using the backpropagation algorithm. In this paper, a modified backpropagation algorithm for a certain and fast training process is presented. The modification is based on the solving of the weight matrix for the output layer using theory of equations and least squares techniques.< >

关键词： backpropagation algorithms Multi-layer neural network Neurons Neural networks Equations Least squares methods

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Benchmarking of the CM-5 and the Cray machines with a very large backpropagation neural network

Benchmarking of the CM-5 and the Cray machines with a very l...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiao Liu G.L. Wilcox Department of Pharmacology and Minnesota Supercomputer Institute University of Minnesota MN USA

In this paper, we present a new, efficient implementation of the backpropagation algorithm (BP) on the CM-5 by fully taking advantage of its Control Network to avoid explicit message-passing. The nodes in the input and output layers are evenly distributed to all processors: all nodes in the hidden layer(s) are replicated in each processor, and all weights are distributed to all processors corresponding to the nodes. We have implemented this algorithm on the CM-5 in the MIMD mode using the C programming language. For a case study of protein tertiary structure prediction, we obtained performance of 76 million weight updates per second (WUPS) with the machine partitioned for 512 processors without vector units. Experiments using different sized partitions indicated an almost linear relationship between the computation time and the number of processors, indicating good parallelization. We have also implemented the backpropagation algorithm on the Cray machines using the C programming language. The Cray-2 implementation yields performance of 10 million WUPS; the Cray X-MP EA implementation yields 18 million WUPS; and the Cray Y-MP M92 implementation yields 40 million WUPS.< >

关键词： Neural networks Proteins Supercomputers backpropagation algorithms Partitioning algorithms Testing Amino acids Biological neural networks Computer languages Concurrent computing

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The causes for premature saturation with backpropagation training

The causes for premature saturation with backpropagation tra...

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International Joint Conference on Neural Networks (IJCNN)

作者： J.E. Vitela J. Reifman Instituto de Ciencias Nucleares Universidad Nacional Autonoma de Mexico Mexicali Mexico Argonne National Laboratory Reactor Analysis Division Argonne IL USA

The mechanism that causes the output nodes of feedforward multilayer networks mapped with sigmoid functions to prematurely saturate during backpropagation training is described. The necessary conditions for the occurrence of this undesirable phenomenon are also presented. Simulation results demonstrate the adequacy of the presented necessary conditions.< >

关键词： Nonhomogeneous media Convergence backpropagation algorithms Laboratories Inductors Iterative algorithms Joining processes Algorithm design and analysis

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Surface reconstruction using robust backpropagation

Surface reconstruction using robust backpropagation

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International Joint Conference on Neural Networks (IJCNN)

作者： D.S. Chen R.C. Jain University of California San Diego USA General Motors Technical Center Warren MI USA

Conventional surface reconstruction methods often either require segmenting data into regions corresponding to piecewise smooth surface patches prior to reconstruction, or have difficulties in preserving discontinuities as well as removing severe noise effects such as outliers. The authors propose a new surface reconstruction method using multilayer feedforward neural networks. The parametric form represented by multilayer neural networks can model piecewise smooth surfaces in a way that is more general and flexible than many of the classical methods. The new approximation method is based upon a robust backpropagation (BP) algorithm, which is resistant to the noise effects and is capable of rejecting gross errors during the approximation process. The spirit of this algorithm comes from the pioneering work in robust statistics by Huber and Hampel. The authors' work is different from that of M-estimators in two aspects: (1) the shape of the objective function changes with the iteration time. (2) The parametric form of the functional approximator is no longer linear. In contrast to the conventional BP algorithm, three advantages of the robust BP algorithm are: (1) it approximates an underlying mapping rather than interpolating training samples; (2) it is robust against gross errors; and (3) its rate of convergence is improved since the influence of incorrect samples is gracefully suppressed.< >

关键词： Surface reconstruction Multi-layer neural network Noise robustness Reconstruction algorithms Neural networks backpropagation algorithms Feedforward neural networks Approximation methods Approximation algorithms Multi-stage noise shaping

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Anti-Hebbian rule for faster backpropagation learning

Anti-Hebbian rule for faster backpropagation learning

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International Joint Conference on Neural Networks (IJCNN)

作者： H.M. Abbas M.M. Bayoumi Department of Electrical Engineering Queen's University Kingston Canada The Computers and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt

This paper introduces an algorithm to speed up the backpropagation learning rules. The algorithm is based on providing lateral connections among the neurons of every hidden layer. These connections are trained using an anti-Hebbian learning rule which decorrelates the outputs of these nodes. The decorrelation process minimizes any redundant information transferred from an internal layer to the next and therefore enables the network to capture the statistical properties of the mapping much faster. The algorithm is applied to some benchmark problems and the results are compared to those obtained using the conventional backpropagation network.< >

关键词： Decorrelation Neurons backpropagation algorithms Cost function Joining processes Neural networks Feedforward neural networks Acceleration Statistics Systems engineering and theory

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