Title :
Stochastic stability of fuzzy Hopfield neural networks with time-varying delays
Author :
Zhu, Chongjun ; Wen, Shiping
Author_Institution :
Coll. of Math. & Stat., Hubei Normal Univ., Huangshi, China
Abstract :
It is well known that a complex nonlinear system can be represented as a Takagi-Sugeno(T-S) Fuzzy model that consists of a set of linear sub-models. This letter is concerned with the global asymptotical stability analysis problem for stochastic fuzzy Hopfield neural networks with successive time delay components. By using the stochastic analysis approach, stability criterion is derived in terms of linear matrix inequalities( LMIs), which can be effectively solved by standard software.
Keywords :
Hopfield neural nets; asymptotic stability; delays; fuzzy set theory; linear matrix inequalities; nonlinear systems; time-varying systems; Takagi-Sugeno fuzzy model; complex nonlinear system; fuzzy Hopfield neural networks; linear matrix inequalities; stability criterion; stochastic stability; time-varying delays; Artificial neural networks; Asymptotic stability; Biological neural networks; Circuit stability; Delay; Stability analysis; Stochastic processes;
Conference_Titel :
Information Science and Technology (ICIST), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9440-8
DOI :
10.1109/ICIST.2011.5765148
