Parallel nonlinear optimization techniques for training neural networks

In this paper, we propose the use of parallel quasi-Newton (QN) optimization techniques to improve the rate of convergence of the training process for neural networks. The parallel algorithms are developed by using the self-scaling quasi-Newton (SSQN) methods. At the beginning of each iteration, a s...

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Bibliographic Details

Published in:	IEEE transactions on neural networks Vol. 14; no. 6; pp. 1460 - 1468
Main Authors:	Phua, P.K.H., Daohua Ming
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.11.2003
Subjects:	Backpropagation algorithms Benchmark testing Character generation Convergence Feedforward neural networks Least squares methods Neural networks Neurons Optimization methods Parallel algorithms
ISSN:	1045-9227
Online Access:	Get full text
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