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Periodic Solution for Fuzzy Cohen–Grossberg BAM Neural Networks with Both Time-Varying and Distributed Delays and Variable Coefficients

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Abstract

In this paper, we investigate the existence and global exponential stability of periodic solution for a general class of fuzzy Cohen–Grossberg bidirectional associative memory (BAM) neural networks with both time-varying and (finite or infinite) distributed delays and variable coefficients. Some novel sufficient conditions for ascertaining the existence, uniqueness, global attractivity and exponential stability of the periodic solution to the considered system are obtained by applying matrix theory, inequality analysis technique and contraction mapping principle. The results remove the usual assumption that the activation functions are bounded and/or continuously differentiable. It is believed that these results are significant and useful for the design and applications of fuzzy Cohen–Grossberg BAM neural networks. Moreover, an example is employed to illustrate the effectiveness and feasibility of the results obtained here.

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Acknowledgments

The author would like to thank the editors and the anonymous referees for their constructive comments and suggestions which led to improvement of the original manuscript.

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Authors and Affiliations

  1. Ministry of Public Education, Sanmenxia Polytechnic, Sanmenxia,  472000, China

    Wengui Yang

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  1. Wengui Yang
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Correspondence to Wengui Yang.

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Yang, W. Periodic Solution for Fuzzy Cohen–Grossberg BAM Neural Networks with Both Time-Varying and Distributed Delays and Variable Coefficients. Neural Process Lett 40, 51–73 (2014). https://doi.org/10.1007/s11063-013-9310-0

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