Dynamical Behavior of Nonautonomous Stochastic Reaction-Diffusion Neural Network Models

Tengda Wei, Ping Lin, Quanxin Zhu, Linshan Wang, Yangfan Wang

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
345 Downloads (Pure)


This brief investigates nonautonomous stochastic reaction-diffusion neural-network models with S-type distributed delays. First, the existence and uniqueness of mild solution are studied under the Lipschitz condition without the linear growth condition. Due to the existence of a nonautonomous reaction-diffusion term and the infinite dimensional Wiener process, the criteria for the well-posedness of the models are established based on the evolution system theory. Then, the S-type distributed delay, which is an infinite delay, is handled by the truncation method, and sufficient conditions for the global exponential stability are obtained by constructing a simple Lyapunov-Krasovskii functional candidate. Finally, neural-network examples and an illustrative example are given to show the applications of the obtained results.

Original languageEnglish
Pages (from-to)1575-1580
Number of pages6
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number5
Early online date26 Sept 2018
Publication statusPublished - May 2019


  • Existence-uniqueness and stability
  • S-type delay.
  • mild solution
  • reaction-diffusion
  • stochastic neural network

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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