On-line Identification Method of Continuous-Time Nonlinear Systems Using Radial Basis Function Network Model Adjusted by Genetic Algorithm

Tomohiro HACHINO  Hitoshi TAKATA  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E87-A   No.9   pp.2372-2378
Publication Date: 2004/09/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
identification,  on-line,  nonlinear systems,  radial basis function networks,  genetic algorithm,  

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This paper deals with an on-line identification method based on a radial basis function (RBF) network model for continuous-time nonlinear systems. The nonlinear term of the objective system is represented by the RBF network. In order to track the time-varying system parameters and nonlinear term, the recursive least-squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA). The centers of the RBF are coded into binary bit strings and searched by the GA, while the system parameters of the linear terms and the weighting parameters of the RBF are updated by the RLS method. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.