A Sequential Learning Algorithm for RBF Networks with Application to Ship Inverse Control

Authors:
Gexin Bi;Fang Dong
Affiliations:
College of Navigation, 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 sequential learning algorithm for constructing radial basis function (RBF) network is introduced referred to as dynamic orthogonal structure adaptation (DOSA) algorithm. The algorithm learns samples sequentially, with both structure and connecting parameters of network are adjusted on-line. The algorithm is further improved by setting initial hidden units and incorporating weight factors. Based on the improved DOSA algorithm, a direct inverse control strategy is introduced and applied to ship control. Simulation results of ship course control simulation demonstrate the applicability and effectiveness of the improved DOSA algorithm and the RBF network-based inverse control strategy.