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Stability criteria for stochastic Takagi‐Sugeno fuzzy Cohen‐Grossberg BAM neural networks with mixed time‐varying delays |
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| Authors: | Muhammed Syed Ali Pagavathigounder Balasubramaniam Fathalla A. Rihan Shanmugam Lakshmanan |
| Affiliation: | 1. Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, India;2. Department of Mathematics, Gandhigram Rural University, Tamil Nadu, India;3. Department of Mathematical Science, College of Science, UAE University, Al‐Ain, UAE;4. Department of Mathematics, Faculty of Science, Helwan University, Cairo, Egypt |
| Abstract: | This article is concerned with the asymptotic stability analysis of Takagi–Sugeno stochastic fuzzy Cohen–Grossberg neural networks with discrete and distributed time‐varying delays. Based on the Lyapunov functional and linear matrix inequality (LMI) technique, sufficient conditions are derived to ensure the global convergence of the equilibrium point. The proposed conditions can be checked easily by LMI Control Toolbox in Matlab. It has been shown that the results are less restrictive than previously known criteria. They are obtained under mild conditions, assuming neither differentiability nor strict monotonicity for activation function. Numerical examples are given to demonstrate the effectiveness of our results. © 2014 Wiley Periodicals, Inc. Complexity 21: 143–154, 2016 |
| Keywords: | Cohen– Grossberg bidirectional associative memory neural network global asymptotic stability linear matrix inequality Lyapunov functional stochastic analysis time‐varying delays T– S fuzzy model |