On-line Identification of Continuous-time Nonlinear Systems Using Radial Basis Function Networks and Genetic Algorithm  

Oleh:

Hachino, T. ; Hasuka, K. ; Takata, H.

Jenis: Article from Article
Dalam koleksi: Final Program and Book of Abstracts: The 4th Asian Control Conference, September 25-27, 2002 (Sep. 2002), page 1736-1740.
Topik: Radial Basis Function; RBF; Genetic Algorithm; Nonlinear Systems
Fulltext: AC021501.PDF (146.22KB)

Isi artikel

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 leastsquares (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.

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