Sw. Stepniewski et Aj. Keane, PRUNING BACKPROPAGATION NEURAL NETWORKS USING MODERN STOCHASTIC OPTIMIZATION TECHNIQUES, NEURAL COMPUTING & APPLICATIONS, 5(2), 1997, pp. 76-98
Citations number
27
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Artificial Intelligence
Approaches combining genetic algorithms and neural networks have recei
ved a great deal of attention in recent years. As a result, much work
has been reported in two major areas of neural network design: trainin
g and topology optimisation. This paper focuses on the key issues asso
ciated with the problem of pruning a multi-layer perceptron using gene
tic algorithms and simulated annealing. The study presented considers
a number of aspects associated with network training that may alter th
e behaviour of a stochastic topology optimiser. Enhancements are discu
ssed that can improve topology searches. Simulation results for the tw
o mentioned stochastic optimisation methods applied to non-linear syst
em identification are presented and compared with a simple random sear
ch.