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A graph-theoretic approach to exponential stability of stochastic BAM neural networks with time-varying delays

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Abstract

This paper investigates the global exponential stability for a stochastic bidirectional associative memory (BAM) neural network with time-varying delays. Based on the principle of graph theory, a new method for pth moment exponential stability is derived by combining some inequalities, Lyapunov method and stochastic analysis. The obtained criteria have close relations to the topology property of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the theoretical results.

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Acknowledgments

The authors really appreciate the reviewers’ valuable comments. This work was supported by the NNSF of China (Nos. 11301115, 11301112, 11271101 and 51208150) and the NSF of Shandong Province (No. ZR2013AQ003).

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Authors and Affiliations

  1. Department of Mathematics, Harbin Institute of Technology (Weihai), Weihai, 264209, People’s Republic of China

    Huan Su, Yuwei Zhao, Wenxue Li & Xiaohua Ding

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  1. Huan Su
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  2. Yuwei Zhao
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  3. Wenxue Li
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  4. Xiaohua Ding
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Correspondence to Huan Su.

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Su, H., Zhao, Y., Li, W. et al. A graph-theoretic approach to exponential stability of stochastic BAM neural networks with time-varying delays. Neural Comput & Applic 27, 2055–2063 (2016). https://doi.org/10.1007/s00521-015-2005-x

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  • DOI: https://doi.org/10.1007/s00521-015-2005-x

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