Stability criteria for stochastic Takagi‐Sugeno fuzzy Cohen‐Grossberg BAM neural networks with mixed time‐varying delays. Issue 5 (30th December 2014)

• Record Type: Journal Article
• Authors: Syed Ali, Muhammed; Balasubramaniam, Pagavathigounder; Rihan, Fathalla A.; Lakshmanan, Shanmugam
• Is Part Of: Complexity. Volume 21:Issue 5(2016)
• Page Start: 143
• Page End: 154
• Publication Date: 2014-12-30
• Subjects: Cohen–Grossberg bidirectional associative memory neural network -- global asymptotic stability -- linear matrix inequality -- Lyapunov functional -- stochastic analysis -- time‐varying delays -- T–S fuzzy model; Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1002/cplx.21642
• Languages: English
• ISSNs: 1076-2787

Abstract

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