Bounds of the induced norm and model reduction errors for a class of nonlinear systems

Author

Chu, Yun-Chung ; Glover, Keith

Author_Institution

Dept. of Eng., Cambridge Univ., UK

Volume

4

fYear

1996

fDate

11-13 Dec 1996

Firstpage

4288

Abstract

The class of nonlinear systems described by a discrete-time state equation containing a diagonal nonlinear term as in recurrent neural networks is considered. Sufficient conditions are derived for the stability and induced norm of such systems using positive definite diagonally dominant Lyapunov functions or storage functions, satisfying appropriate linear matrix inequalities. Preliminary results are also presented for model reduction errors for such systems

Keywords

asymptotic stability; discrete time systems; matrix algebra; nonlinear systems; recurrent neural nets; reduced order systems; discrete-time state equation; induced norm; linear matrix inequalities; model reduction errors; nonlinear systems; positive definite diagonally dominant Lyapunov functions; recurrent neural networks; stability; storage functions; Artificial neural networks; Linear matrix inequalities; Lyapunov method; Neural networks; Nonlinear equations; Nonlinear systems; Recurrent neural networks; Reduced order systems; Stability; Student awards;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on

Conference_Location

Kobe

ISSN

0191-2216

Print_ISBN

0-7803-3590-2

Type

conf

DOI

10.1109/CDC.1996.577462

Filename

577462