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Pruning back propagation neural networks using modern stochastic optimization techniques

Record type: Article

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.

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Citation

Stepniewski, Slawomir W. and Keane, Andy J. (1997) Pruning back propagation neural networks using modern stochastic optimization techniques Neural Computing and Applications, 5, (2), pp. 76-98. (doi:10.1007/BF01501173).

More information

Published date: 1997

Identifiers

Local EPrints ID: 21081
URI: http://eprints.soton.ac.uk/id/eprint/21081
PURE UUID: 30d812ea-6ace-4572-b801-f29af456c72e

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Date deposited: 31 Oct 2006
Last modified: 17 Jul 2017 16:27

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Contributors

Author: Slawomir W. Stepniewski
Author: Andy J. Keane

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