On-line identification of nonlinear systems using Volterra polynomial basis function neural networks

被引：43

作者：

Liu, GP ^{[1
]}

Kadirkamanathan, V

Billings, SA

机构：

[1] ALSTOM, Energy Technol Ctr, Leicester LE8 6LU, Leics, England

[2] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

来源：

NEURAL NETWORKS | 1998年 / 11卷 / 09期

基金：

英国工程与自然科学研究理事会;

关键词：

neural networks; nonlinear system identification; recursive weighting learning; growing network; Volterra polynomials; orthogonal least-squares algorithm;

D O I：

10.1016/S0893-6080(98)00100-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An on-line identification scheme using Volterra polynomial basis function (VPBF) neural networks is considered for nonlinear control systems. This comprises a structure selection procedure and a recursive weight learning algorithm. The orthogonal least-squares algorithm is introduced for off-line structure selection and the growing network technique is used for on-line structure selection. An on-line recursive weight learning algorithm is developed to adjust the weights so that the identified model can adapt to variations of the characteristics and operating points in nonlinear systems. The convergence of both the weights and the estimation errors is established using a Lyapunov technique. The identification procedure is illustrated using simulated examples. (C) 1998 Elsevier Science Ltd. All rights reserved.

引用

页码：1645 / 1657

页数：13

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