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), 76-98. (doi:10.1007/BF01501173).

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Original Publication URL: http://dx.doi.org/10.1007/BF01501173

Description/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.

Item Type: Article
ISSNs: 0941-0643 (print)
Related URLs:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: University Structure - Pre August 2011 > School of Engineering Sciences
ePrint ID: 21081
Date Deposited: 31 Oct 2006
Last Modified: 27 Mar 2014 18:10
Contact Email Address: ajk@soton.ac.uk
URI: http://eprints.soton.ac.uk/id/eprint/21081

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