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Pruning back propagation neural networks using modern stochastic optimization techniques

By Slawomir W. Stepniewski and Andy J. Keane

Abstract

Approaches combining genetic algorithms and neural networks have received a great deal of attention in recent years. As a result, much work has been reported in two major areas of neural network design: training and topology optimisation. This paper focuses on the key issues associated with the problem of pruning a multilayer perceptron using genetic algorithms and simulated annealing. The study presented considers a number of aspects associated with network training that may alter the behaviour of a stochastic topology optimiser. Enhancements are discussed that can improve topology searches. Simulation results for the two mentioned stochastic optimisation methods applied to non-linear system identification are presented and compared with a simple random search

Topics: QA75
Year: 1997
OAI identifier: oai:eprints.soton.ac.uk:21081
Provided by: e-Prints Soton

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