A Hybrid Model of Partial Least Squares and RBF Neural Networks for System Identification

Authors:
Nini Wang;Xiaodong Liu;Jianchuan Yin
Affiliations:
Research Center of information and Control, Dalian University of Technology, Dalian, China 116024 and Department of Mathematics, Dalian Maritime University, Dalian, China 116026;Research Center of information and Control, Dalian University of Technology, Dalian, China 116024 and Department of Mathematics, Dalian Maritime University, Dalian, China 116026;College of Navigation, Dalian Maritime University, Dalian, China 116026
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

A novel learning algorithm is presented to construct radial basis function (RBF) networks by incorporating partial least squares (PLS) regression method. The algorithm selects hidden units one by one with PLS regression method until an adequate network is achieved, and the resulting minimal RBF-PLS (MRBF-PLS) network exhibits satisfying generalization performance and noise toleration capability. The algorithm provides an efficient approach for system identification, and this is illustrated by modelling nonlinear function and chaotic time series.