State of the art in nonlinear dynamical system identification using Artificial Neural Networks

被引：0
|
作者：
Todorovic, Nenad
Klan, Petr
机构：
来源：
NEUREL 2006: EIGHT SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS | 2006年
关键词：
artificial neural networks; nonlinear dynamical systems; nonlinear identification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper covers the state of the art in nonlinear dynamical system identification using Artificial Neural Networks (ANN). The main approaches in the last two decades are presented in unified framework. ANN have unique characteristics, which enable them to model nonlinear dynamical systems. The main problems with the choice of ANN model structure are considered and commonly used identification schemes are proposed. A procedure for derivation of parameter estimation law using Lyapunov synthesis approach, which guarantees stability and convergence of the overall identification scheme, is presented.
引用
收藏
页码：103 / 108
页数：6
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