New robust stability criteria for neutral-type neural networks with multiple mixed delays

Author

Jin, Li

Author_Institution

Dept. of Math., Dalian Jiaotong Univ., Dalian, China

Volume

1

fYear

2010

fDate

13-14 Sept. 2010

Firstpage

244

Lastpage

247

Abstract

The global exponential stability is analyzed for a class of uncertain neutral-type neural networks with multiple variable and distributed delays. By applying Jensen integral inequality, free-weighting matrix method and linear matrix inequality(LMI) techniques, some less conservative delay-dependent stability criteria are obtained, which generalize some previous results in the literature. Furthermore, the obtained results can be generalized to uncertain neural networks and bidirectional associative memory (BAM) neural networks.

Keywords

asymptotic stability; content-addressable storage; delays; linear matrix inequalities; neural nets; robust control; uncertain systems; Jensen integral inequality; bidirectional associative memory neural network; delay dependent stability criteria; distributed delay; free-weighting matrix method; global exponential stability; linear matrix inequality; multiple mixed delay; robust stability criteria; uncertain neutral type neural networks; Artificial neural networks; Delay; Linear matrix inequalities; Robustness; Stability criteria; Symmetric matrices; Bidirectional associative mem-ory(BAM) neural networks; Global robust exponential stability; Jensen integral inequality; free-weighting matrix method; linear matrix inequality(LMI); neutral-type;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Natural Computing Proceedings (CINC), 2010 Second International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-7705-0

Type

conf

DOI

10.1109/CINC.2010.5643847

Filename

5643847