On-line identification method of continuous-time nonlinear systems using radial basis function network model adjusted by genetic algorithm

被引：0

作者：

Hachino, T ^{[1
]}

Takata, H ^{[1
]}

机构：

[1] Kagoshima Univ, Dept Elect & Elect Engn, Kagoshima 8900065, Japan

来源：

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | 2004年 / E87A卷 / 09期

关键词：

identification; on-line; nonlinear systems; radial basis function networks; genetic algorithm;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2372 / 2378

页数：7

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