An On-Line Learning Radial Basis Function Network and Its Application

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

To improve the on-line predictive capability of radial basis function (RBF) networks, a novel sequential learning algorithm is developed referred to as sequential orthogonal model selection (SOMS) algorithm. The RBF network is adapted on-line for both network structure and connecting parameters. Based on SOMS algorithm, a multi-step predictive control strategy is introduced and applied to ship control. Simulation results of ship course control experiment demonstrate the applicability and effectiveness of the SOMS algorithm.