DocumentCode
3110814
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
fYear
2011
fDate
26-28 March 2011
Firstpage
1034
Lastpage
1037
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Technology (ICIST), 2011 International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-9440-8
Type
conf
DOI
10.1109/ICIST.2011.5765148
Filename
5765148
Link To Document