NDLI: A Novel Sequential Learning Algorithm for RBF Networks and Its Application to Ship Predictive Control

Content Provider	IEEE Xplore Digital Library
Author	Jianchuan Yin Fang Dong Nini Wang
Copyright Year	2006
Description	Author affiliation: Dalian Maritime Univ., Dalian (Jianchuan Yin; Fang Dong; Nini Wang)
Abstract	A radial basis function (RRF) network -based predictive control strategy is proposed for ship control. The RBF network is on-line trained to identify the time-varying system dynamics using a novel sequential learning algorithm referred in as dynamic orthogonal structure adaptation (DOSA) algorithm. The combination of neural network identification and predictive control mechanism minimizes the effects of ships time-varying dynamics and long-time delay, enables accurate and smooth control of ship under various disturbances and. random noises. Simulation results of ship track-keeping control demonstrate the applicability and effectiveness of the control strategy. The quick and adaptive learning algorithm gives RBF network more representing abilities to model nonlinear systems with unstable or unknown dynamics.
Starting Page	4690
Ending Page	4696
File Size	2729542
Page Count	7
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247122
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radial basis function networks Marine vehicles Predictive control Delay effects Neural networks Process control Predictive models Control systems Nonlinear dynamical systems Least squares methods
Content Type	Text
Resource Type	Article

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