Robust stability of fuzzy Markov type Cohen-Grossberg neural networks by delay decomposition approach

Document Type : Research Paper

Authors

1 Department of Social Sciences, Tamil Nadu Agricultural University, Coim- batore - 641 003, Tamilnadu, India

2 Department of Mathematics, Gandhigram Rural Institute - Deemed University, Gandhigram - 624 302, Tamilnadu, India

3 Department of Computer Science, Government Arts College, Melur, Madurai - 625 106, Tamilnadu, India

Abstract

In this paper, we investigate the delay-dependent robust stability of fuzzy Cohen-Grossberg neural networks with Markovian jumping parameter and mixed time varying delays by delay decomposition method. A new Lyapunov-Krasovskii functional (LKF) is constructed by nonuniformly dividing discrete delay interval into multiple subinterval, and choosing proper functionals with different weighting matrices corresponding to different subintervals in the LKFs. A new delay-dependent stability condition is derived with Markovian jumping parameters by T-S fuzzy model. Based on the linear matrix inequality (LMI) technique, maximum admissible upper bound (MAUB) for the discrete and distributed delays are calculated by the LMI Toolbox in MATLAB. Numerical examples are given to illustrate the effectiveness of the proposed method.

Keywords

References

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Iranian Journal of Fuzzy Systems
Volume 11, Issue 2 - Serial Number 2
March and April 2014
Pages 1-16

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