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Almost periodic solutions in distribution of Clifford-valued stochastic recurrent neural networks with time-varying delays

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  • Li, Yongkun
  • Wang, Xiaohui

Abstract

This paper uses the method of non-decomposition to study the existence and global exponential stability of almost periodic solutions in distribution for a class of Clifford-valued stochastic recurrent neural networks with time-varying delays. Firstly, Banach fixed point theorem and inequality techniques are used to establish the existence and uniqueness of almost periodic solutions in distribution of this class of neural networks. Then, the global exponential stability of this unique almost periodic solution in distribution is proved by the contradiction method. Our results and the method of dealing with time-varying delays are new. Finally, an example is given to illustrate the feasibility of our results.

Suggested Citation

  • Li, Yongkun & Wang, Xiaohui, 2021. "Almost periodic solutions in distribution of Clifford-valued stochastic recurrent neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    • Handle: RePEc:eee:chsofr:v:153:y:2021:i:p2:s0960077921008900
    DOI: 10.1016/j.chaos.2021.111536
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    References listed on IDEAS

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    Cited by:

    1. Weiwei Qi & Yongkun Li, 2022. "Almost Anti-Periodic Oscillation Excited by External Inputs and Synchronization of Clifford-Valued Recurrent Neural Networks," Mathematics, MDPI, vol. 10(15), pages 1-12, August.

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