Stability criteria for stochastic Takagi-Sugeno fuzzy Cohen-Grossberg BAM neural networks with mixed time-varying delays

Muhammed Syed Ali; Pagavathigounder Balasubramaniam; Fathalla A. Rihan; Shanmugam Lakshmanan

doi:10.1002/cplx.21642

Stability criteria for stochastic Takagi-Sugeno fuzzy Cohen-Grossberg BAM neural networks with mixed time-varying delays

Muhammed Syed Ali, Pagavathigounder Balasubramaniam, Fathalla A. Rihan, Shanmugam Lakshmanan

Department of Mathematical Sciences

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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.

Original language	English
Pages (from-to)	143-154
Number of pages	12
Journal	Complexity
Volume	21
Issue number	5
DOIs	https://doi.org/10.1002/cplx.21642
Publication status	Published - May 1 2016

Keywords

Cohen-Grossberg bidirectional associative memory neural network
Global asymptotic stability
Linear matrix inequality
Lyapunov functional
Stochastic analysis
T-S fuzzy model
Time-varying delays

ASJC Scopus subject areas

Computer Science(all)
General

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10.1002/cplx.21642

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title = "Stability criteria for stochastic Takagi-Sugeno fuzzy Cohen-Grossberg BAM neural networks with mixed time-varying delays",

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.",

keywords = "Cohen-Grossberg bidirectional associative memory neural network, Global asymptotic stability, Linear matrix inequality, Lyapunov functional, Stochastic analysis, T-S fuzzy model, Time-varying delays",

author = "{Syed Ali}, Muhammed and Pagavathigounder Balasubramaniam and Rihan, {Fathalla A.} and Shanmugam Lakshmanan",

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AU - Syed Ali, Muhammed

AU - Balasubramaniam, Pagavathigounder

AU - Rihan, Fathalla A.

AU - Lakshmanan, Shanmugam

PY - 2016/5/1

Y1 - 2016/5/1

N2 - 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.

AB - 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.

KW - Cohen-Grossberg bidirectional associative memory neural network

KW - Global asymptotic stability

KW - Linear matrix inequality

KW - Lyapunov functional

KW - Stochastic analysis

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KW - Time-varying delays

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Stability criteria for stochastic Takagi-Sugeno fuzzy Cohen-Grossberg BAM neural networks with mixed time-varying delays

Abstract

Keywords

ASJC Scopus subject areas

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