Title :
Stochastic stability analysis of fuzzy hopfield neural networks with time-varying delays
Author :
Huang, He ; Ho, Daniel W C ; Lam, James
Author_Institution :
Dept. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China
fDate :
5/1/2005 12:00:00 AM
Abstract :
The ordinary Takagi-Sugeno (TS) fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied. The model of SFVDHNN is first established as a modified TS fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays. Secondly, the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach. Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages.
Keywords :
Hopfield neural nets; Lyapunov methods; asymptotic stability; delay systems; fuzzy neural nets; linear matrix inequalities; stochastic systems; Lyapunov-Krasovskii approach; fuzzy Hopfield neural networks; fuzzy systems; global exponential stability; linear matrix inequalities; stability criterion; stochastic Hopfield neural networks; stochastic stability analysis; stochastic systems; time-varying delay systems; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Hopfield neural networks; Nonlinear systems; Stability analysis; Stability criteria; Stochastic processes; Takagi-Sugeno model; Fuzzy systems; Hopfield neural networks; stability; stochastic systems; time-varying delay systems;
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
DOI :
10.1109/TCSII.2005.846305
