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Global Asymptotic Periodic Synchronization for Delayed Complex-Valued BAM Neural Networks via Vector-Valued Inequality Techniques

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Abstract

In this paper, we are concerned with a class of delayed complex-valued BAM neural networks. In stead of using the priori estimate method of periodic solutions, by means of combining Mawhin’s continuation theorem of coincidence degree theory with novel LMI method and some analysis techniques, a novel LMI-based sufficient condition is obtained for the existence of periodic solutions of the delayed complex-valued BAM neural networks. Then by using novel LMI method, a novel sufficient condition on global asymptotic periodic synchronization of above complex-valued BAM neural networks is established.

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Authors and Affiliations

  1. College of Mathematics and Econometrics, Hunan University, Changsha, 410082, People’s Republic of China

    Zhengqiu Zhang, Ailing Li & Lin Yang

Authors
  1. Zhengqiu Zhang
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  2. Ailing Li
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  3. Lin Yang
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Corresponding author

Correspondence to Lin Yang.

Additional information

Project supported by the Innovation Platform Open Fund in Hunan Province Colleges and Universities of China (No. 201485).

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Zhang, Z., Li, A. & Yang, L. Global Asymptotic Periodic Synchronization for Delayed Complex-Valued BAM Neural Networks via Vector-Valued Inequality Techniques. Neural Process Lett 48, 1019–1041 (2018). https://doi.org/10.1007/s11063-017-9722-3

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  • DOI: https://doi.org/10.1007/s11063-017-9722-3

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