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Stability Analysis of Stochastic Fuzzy Neural Networks with Time-Varying Delays and Reaction–Diffusion Terms

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Abstract

This paper investigates the problem of the global exponential stability for a class of stochastic Takagi–Sugeno fuzzy neural networks (STSFNNs) with time-varying delays and reaction–diffusion terms. Based on the piecewise Lyapunov–Krasovskii functional, Poincaré integral inequality, and Itô differential formula, we obtain some sufficient conditions ensuring the global exponential stability of an equilibrium point for STSFNNs with time-varying delays and reaction–diffusion terms. These sufficient conditions depend on the reaction–diffusion terms and time delays. Finally, some examples are given to show the effectiveness and superiority of the proposed approach.

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Acknowledgements

The work described in this paper was supported in part by the National Natural Science Foundation of China (Grant Nos. 61374117, 61174137, and 61104064), the 973 project (Grant No. 2011CB707000), the NSF of Jiang Su Province (Grant No. BK2010493).

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Authors and Affiliations

  1. Centre for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China

    Hongyu Zhang, Chenhui Zhou, Hongbin Zhang & Gang Wang

  2. Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P.R. China

    Qichong Tian

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  1. Hongyu Zhang
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  2. Chenhui Zhou
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Correspondence to Chenhui Zhou.

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Zhang, H., Zhou, C., Zhang, H. et al. Stability Analysis of Stochastic Fuzzy Neural Networks with Time-Varying Delays and Reaction–Diffusion Terms. Circuits Syst Signal Process 33, 713–732 (2014). https://doi.org/10.1007/s00034-013-9667-3

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