Global asymptotic stability of stochastic fuzzy cellular neural networks with multiple time-varying delays

2010 ◽  
Vol 37 (12) ◽  
pp. 7737-7744 ◽  
Author(s):  
P. Balasubramaniam ◽  
M. Syed Ali ◽  
Sabri Arik
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Global Asymptotic Stability ◽  
Cellular Neural Networks ◽  
Multiple Time ◽  
Time Varying ◽  
Fuzzy Cellular Neural Networks

Global Asymptotic Stability of Stochastic Fuzzy Cellular Neural Networks with Time-Varying Delays

2010 ◽  
Vol 139-141 ◽  
pp. 1714-1717
Author(s):  
Wen Guang Luo ◽  
Yong Hua Liu ◽  
Hong Li Lan
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Global Asymptotic Stability ◽  
Stability Result ◽  
Cellular Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Fuzzy Cellular Neural Networks ◽  
The Stability ◽  
Delay Dependent

In this paper, the problem of global asymptotic stability in the mean square for stochastic fuzzy cellular neural networks (SFCNN) with time-varying delays is investigated. By constructing a newly proposed Lyapunov-Krasovskii function (LKF) and using Ito’s stochastic stability theory, a novel delay-dependent stability criterion is derived. The obtained stability result is helpful to design the stability of fuzzy cellular neural networks (FCNN) with time-varying delays when stochastic noise is taken into consideration. Since it is presented in terms of a linear matrix inequality (LMI), the sufficient condition is easy to be checked efficiently by utilizing some standard numerical packages such as the LMI Control Toolbox in Matlab. Finally, an illustrate example is given to verify the feasibility and usefulness of the proposed result.

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