DocumentCode
1483067
Title
Bounds of the induced norm and model reduction errors for systems with repeated scalar nonlinearities
Author
Chu, Yun-Chung ; Glover, Keith
Author_Institution
Dept. of Eng., Cambridge Univ., UK
Volume
44
Issue
3
fYear
1999
fDate
3/1/1999 12:00:00 AM
Firstpage
471
Lastpage
483
Abstract
The class of nonlinear systems described by a discrete-time state equation containing a repeated scalar nonlinearity 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. Results are also presented for model reduction errors for such systems
Keywords
Lyapunov methods; discrete time systems; matrix algebra; nonlinear systems; recurrent neural nets; reduced order systems; stability; Lyapunov functions; diagonal stability; discrete-time systems; induced norm; linear matrix inequality; model reduction errors; nonlinear systems; recurrent neural networks; repeated scalar nonlinearities; sufficient conditions; Artificial neural networks; Linear matrix inequalities; Linear systems; Nonlinear equations; Nonlinear systems; Observability; Recurrent neural networks; Reduced order systems; Stability; Sufficient conditions;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/9.751342
Filename
751342
Link To Document