Periodic Solutions to Stochastic Reaction-Diffusion Neural Networks With S-Type Distributed Delays

In this paper, the existence and stability of mild periodic solutions to the stochastic reaction–diffusion neural networks (SRDNNs) with S-type distributed delays are studied. First, the key issues of the Markov property of mild solutions to the SRDNNs with S-type distributed delays in $C^{b}$ -space are investigated. Next, the existence of mild periodic solutions is discussed by the dissipative theory and the operator semigroup theory. Then, some sufficient conditions ensuring the stability of mild periodic solutions are derived by the Lyapunov method. To overcome the difficulties created by the special features possessed by S-type distributed delays, the truncation method is applied. Finally, a numerical example is given to illustrate the feasibility of our results.

• Publication year

  2019

• Venue

  IEEE Access

• Publication date

  2019-04-18

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/ACCESS.2019.2911962
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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