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Global Asymptotic Stability of Periodic Solutions for Neutral-Type BAM Neural Networks with Delays

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Abstract

In this paper, we study the neutral-type BAM neural networks with time-varying delays. By applying the continuation theorem and some analysis techniques, some sufficient conditions to guarantee the neutral-type BAM neural networks have at least one periodic solution are proposed. Moreover, we also consider the asymptotic behaviours of periodic solutions by Lyapunov function and inequality \(2ab\le a^{2}+b^{2}\). At last, an example is given to illustrate the effectiveness and feasibility of the obtain results.

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Acknowledgements

The authors would like to thank the editor and reviewers for their insightful and constructive comments. This work was supported by the NSFC (11571088, 11471109), the Zhejiang Provincial Natural Science Foundation of China ( LY14A010024), and Scientific Research Fund of Hunan Provincial Education Department ( 14A098).

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  1. Key Laboratory of High Performance Computing and Stochastic Information, Processing (HPCSIP) (Ministry of Education of China), College of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, Hunan, People’s Republic of China

    Dongdong Gao & Jianli Li

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  1. Dongdong Gao
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  2. Jianli Li
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Correspondence to Jianli Li.

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Gao, D., Li, J. Global Asymptotic Stability of Periodic Solutions for Neutral-Type BAM Neural Networks with Delays. Neural Process Lett 51, 367–382 (2020). https://doi.org/10.1007/s11063-019-10092-y

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