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Pruning back propagation neural networks using modern stochastic optimization techniques

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).

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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Published date: 1997


Local EPrints ID: 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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Author: Slawomir W. Stepniewski
Author: Andy J. Keane

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