Abstract

This paper proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms; the first term is to discourage the use of unnecessary connections and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well known problems. These test problems are the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature. 1 Introduction We are concerned in this paper with finding a minimal feedforward backpropagation neural network for solving the problem of distinguishing patterns from two or more sets in n-dimensional space. Backpropagation feedforward neural networks have been gaining ac...

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