Automatic design of hierarchical RBF networks for system identification

Authors:
Yuehui Chen;Bo Yang;Jin Zhou
Affiliations:
School of Information Science and Engineering, Jinan University, Jinan, P.R. China;School of Information Science and Engineering, Jinan University, Jinan, P.R. China and State Key Lab. of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Science and ...;School of Information Science and Engineering, Jinan University, Jinan, P.R. China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 7
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Abstract

The purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically. Based on the pre-defined instruction/operator sets, a hierarchical RBF neural network is created and evolved by using Extended Compact Genetic Programming (ECGP), and the parameters are optimized by Differential Evolution (DE) algorithm. Empirical results on benchmark system identification problems indicate that the proposed method is efficient.