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Random Attractor of Reaction-Diffusion Hopfield Neural Networks Driven by Wiener Processes

Author

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  • Xiao Liang
  • Linshan Wang
  • Ruili Wang

Abstract

This paper studies the global existence and uniqueness of the mild solution for reaction-diffusion Hopfield neural networks (RDHNNs) driven by Wiener processes by applying a Schauder fixed point theorem and a priori estimate; then the random attractor for this system is also studied by constructing proper random dynamical system.

Suggested Citation

  • Xiao Liang & Linshan Wang & Ruili Wang, 2018. "Random Attractor of Reaction-Diffusion Hopfield Neural Networks Driven by Wiener Processes," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, January.
    • Handle: RePEc:hin:jnlmpe:2538658
    DOI: 10.1155/2018/2538658
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    Cited by:

    1. Xiongrui Wang & Ruofeng Rao & Shouming Zhong, 2020. "p th Moment Stability of a Stationary Solution for a Reaction Diffusion System with Distributed Delays," Mathematics, MDPI, vol. 8(2), pages 1-10, February.

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