Constructive and pruning methods for neural network design

Abstract

This paper presents methods to improve generalization of multilayer perceptron (MLP) by pruning the original topology without loss in performance. Topology information and validation sets are used. The results show that these techniques are able to choose a minimum network topology and to simplify trained networks.