A formal selection and pruning algorithm for feedforward artificial neural network optimization

作     者：Ponnapalli, PVS Ho, KC Thomson, M 

作者机构：Manchester Metropolitan Univ Dept Elect & Elect Engn Manchester M1 5GD Lancs England 

出 版 物：《IEEE TRANSACTIONS ON NEURAL NETWORKS》 (IEEE Trans Neural Networks)

年 卷 期：1999年第10卷第4期

页      面：964-968页

核心收录：

主　　题：backpropagation feedforward artificial neural network optimization pruning 

摘      要：A formal selection and pruning technique based on the concept of local relative sensitivity index is proposed for feedforward artificial neural networks. The mechanism of backpropagation training algorithm: is revisited and the theoretical foundation of the improved selection and pruning technique is presented. This technique Is based on parallel pruning of weights which are relatively redundant in a subgroup of a feedforward neural network. Comparative studies with a similar technique proposed in the literature show that the improved technique provides better pruning results in terms of reduction of model residues, improvement, of generalization capability and reduction of network complexity;The effectiveness of the improved technique is demonstrated in developing neural network (NN) models of a number of nonlinear systems including three bit parity problem, Van der Pol equation, a chemical processes and two: nonlinear discrete-time systems using the backpropagation training algorithm with adaptive learning late.