Paper Info
Title
Exponential and almost sure exponential stability of stochastic fuzzy delayed Cohen-Grossberg neural networks
Abstract
In this paper, we study a class of stochastic fuzzy delayed Cohen-Grossberg neural networks. Two kinds of stability are discussed in our investigation. One is exponential stability in the mean square and the other is almost sure exponential stability. First, some sufficient conditions are derived to guarantee the exponential stability in the mean square for the considered system based on the Lyapunov-Krasovskii functional, stochastic analysis theory and the Ito's formula as well as the Dynkin formula. Then, we further investigate the almost sure exponential stability by employing the nonnegative semi-martingale convergence theorem. Moreover, we prove that the addressed system is both almost sure exponentially stable and exponentially stable in the mean square under suitable conditions. Finally, three numerical examples are also given to show the effectiveness of the theoretical results. In particular, the simulation figures establish that fuzzy systems do have more advantages than non-fuzzy systems.
Year
DOI
Venue
2012
10.1016/j.fss.2012.01.005
Fuzzy Sets and Systems
Keywords
Field
DocType
considered system,exponential stability,non-fuzzy system,sure exponential stability,fuzzy system,fuzzy delayed cohen-grossberg neural,exponentially stable,mean square,sure exponentially stable,dynkin formula,fuzzy neural network
Convergence (routing),Mean square,Applied mathematics,Discrete mathematics,Exponential function,Mathematical analysis,Fuzzy logic,Stochastic process,Exponential stability,Fuzzy control system,Artificial neural network,Mathematics
Journal
Volume
ISSN
Citations 
203,
0165-0114
49
PageRank 
References 
Authors
1.33
24
2
Name
Order
Citations
PageRank
Quanxin Zhu1110067.69
Xiaodi Li241020.60