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Self-excitation of neurons leads to multiperiodicity of discrete-time neural networks with distributed delays

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Abstract

In this paper, we investigate the interesting multiperiodicity of discrete-time neural networks with excitatory self-connections and distributed delays. Due to self-excitation of neurons, we construct 2N close regions in state space for N-dimensional networks and attain the coexistence of 2N periodic sequence solutions in these close regions. Meanwhile we estimate exponential attracting domain for each periodic sequence solution and apply our results to discrete-time analogues of periodic or autonomous neural networks. Under self-excitation of neurons, numerical simulations are performed to illustrate the effectiveness of our results.

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Authors and Affiliations

  1. School of Sciences, Jimei University, Xiamen, 361021, China

    ZhenKun Huang

  2. Department of Mathematics, Zhejiang University, Hangzhou, 310027, China

    XingHua Wang

  3. Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Gadong, BE 1410, Brunei Darussalam

    Mohamad Sannay

Authors
  1. ZhenKun Huang
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  2. XingHua Wang
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  3. Mohamad Sannay
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Correspondence to ZhenKun Huang.

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Huang, Z., Wang, X. & Sannay, M. Self-excitation of neurons leads to multiperiodicity of discrete-time neural networks with distributed delays. Sci. China Inf. Sci. 54, 305–317 (2011). https://doi.org/10.1007/s11432-010-4148-9

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  • DOI: https://doi.org/10.1007/s11432-010-4148-9

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