New results on the stability of fuzzy cellular neural networks with time-varying leakage delays

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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Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays

International Journal of Applied Mathematics and Computer Science ◽

10.2478/v10006-011-0051-9 ◽

2011 ◽

Vol 21 (4) ◽

pp. 649-658 ◽

Cited By ~ 6

Author(s):

Qianhong Zhang ◽

Lihui Yang ◽

Daixi Liao

Keyword(s):

Neural Networks ◽

Periodic Solution ◽

Exponential Stability ◽

Differential Inequality ◽

Sufficient Conditions ◽

Coincidence Degree ◽

Cellular Neural Networks ◽

Time Varying ◽

Fuzzy Cellular Neural Networks ◽

Inequality Technique

Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays Fuzzy cellular neural networks with time-varying delays are considered. Some sufficient conditions for the existence and exponential stability of periodic solutions are obtained by using the continuation theorem based on the coincidence degree and the differential inequality technique. The sufficient conditions are easy to use in pattern recognition and automatic control. Finally, an example is given to show the feasibility and effectiveness of our methods.

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ATTRACTABILITY AND LOCATION OF EQUILIBRIUM POINT OF CELLULAR NEURAL NETWORKS WITH TIME-VARYING DELAYS

International Journal of Neural Systems ◽

10.1142/s0129065704002054 ◽

2004 ◽

Vol 14 (05) ◽

pp. 337-345 ◽

Cited By ~ 7

Author(s):

ZHIGANG ZENG ◽

DE-SHUANG HUANG ◽

ZENGFU WANG

Keyword(s):

Neural Networks ◽

Exponential Stability ◽

Equilibrium Point ◽

Global Exponential Stability ◽

Cellular Neural Networks ◽

Stability Conditions ◽

Time Varying ◽

The Stability ◽

Theoretical Results

This paper presents new theoretical results on global exponential stability of cellular neural networks with time-varying delays. The stability conditions depend on external inputs, connection weights and delays of cellular neural networks. Using these results, global exponential stability of cellular neural networks can be derived, and the estimate for location of equilibrium point can also be obtained. Finally, the simulating results demonstrate the validity and feasibility of our proposed approach.

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