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The existence and exponential attractivity of κ-almost periodic sequence solution of discrete time neural networks

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Abstract

In the present paper, several sufficient conditions are obtained for the existence and exponential attractivity of a unique κ-almost periodic sequence solution of discrete time neural network. Our results generalize the corresponding results about almost periodic sequence solution in common sense. It is shown that discretization step κ affects the dynamical characteristics of discrete-time analogues of continuous time neural networks and exponential convergence is dependent on small discretization step size. Our results on exponential attractivity of κ-almost periodic sequence solution can provide us with relevant estimates on how precise such networks can perform during real-time computations. Finally, computer simulations are performed in the end to show the feasibility of our results.

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Authors and Affiliations

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

    Zhenkun Huang & Xinghua Wang

  2. School of Sciences, Jimei University, Xiamen, Fujian, 361021, China

    Zhenkun Huang

  3. College of Mathematics and Computer Science, Fuzhou University, Fujian, 350002, China

    Yonghui Xia

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  1. Zhenkun Huang
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  2. Yonghui Xia
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  3. Xinghua Wang
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Correspondence to Yonghui Xia.

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Huang, Z., Xia, Y. & Wang, X. The existence and exponential attractivity of κ-almost periodic sequence solution of discrete time neural networks. Nonlinear Dyn 50, 13–26 (2007). https://doi.org/10.1007/s11071-006-9139-4

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  • DOI: https://doi.org/10.1007/s11071-006-9139-4

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