Delay-dependent exponential stability analysis of fuzzy delayed Hopfield neural networks: A fuzzy Lyapunov-Krasovskii functional approach

Author

Sheng, Li ; Yang, Huizhong

Author_Institution

Sch. of Commun. & Control Eng., Jiangnan Univ., Wuxi, China

fYear

2009

fDate

10-12 June 2009

Firstpage

4296

Lastpage

4301

Abstract

This paper investigates the delay-dependent exponential stability problem of Takagi-Sugeno (TS) fuzzy Hopfield neural networks (HNNs) with time-varying delay. Based on a fuzzy Lyapunov-Krasovskii functional (LKF), some delay-dependent stability criteria guaranteeing the exponential stability of the fuzzy HNNs are devised by taking the relationship between the terms in the Leibniz-Newton formula into account. Since free weighting matrices are used to express this relationship and the appropriate ones are selected by means of linear matrix inequalities (LMIs), the criteria are less conservative than existing ones reported in the literature for delayed fuzzy neural networks. A simulation example is provided to illustrate the effectiveness of the developed method.

Keywords

Hopfield neural nets; Lyapunov methods; Newton method; asymptotic stability; delays; fuzzy control; fuzzy neural nets; linear matrix inequalities; neurocontrollers; time-varying systems; LMI; Leibniz-Newton formula; Takagi-Sugeno fuzzy delayed Hopfield neural network; delay-dependent exponential stability analysis; free weighting matrix approach; fuzzy Lyapunov-Krasovskii functional approach; linear matrix inequality; numerical analysis; time-varying delay; Delay effects; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Hopfield neural networks; Linear matrix inequalities; Neural networks; Stability analysis; Stability criteria;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2009. ACC '09.

Conference_Location

St. Louis, MO

ISSN

0743-1619

Print_ISBN

978-1-4244-4523-3

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2009.5159889

Filename

5159889