A structure optimisation algorithm for feedforward neural network construction

Authors:
Hong-Gui Han;Jun-Fei Qiao
Affiliations:
College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China;College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
Venue:
Neurocomputing
Year:
2013

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Abstract

This paper proposes a constructing-and-pruning (CP) approach to optimise the structure of a feedforward neural network (FNN) with a single hidden layer. The number of hidden nodes or neurons is determined by their contribution ratios, which are calculated using a Fourier decomposition of the variance of the FNN's output. Hidden nodes with sufficiently small contribution ratios will be eliminated, while new nodes will be added when the FNN cannot satisfy certain design objectives. This procedure is similar to the growing and pruning processes observed in biological neural networks. The performance of the proposed method is evaluated using a number of examples: real-life date classification, dynamic system identification, and the key variables modelling in a wastewater treatment system. Experimental results show that the proposed method effectively optimises the network structure and performs better than some existing algorithms.