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Abstract This paper considers the mean square exponential input-to-state stability (EISS) for stochastic delay reaction-diffusion neural networks (SDRDNNS). SDRDNNS with distributed input and boundary input are investigated. In addition, constant… Click to show full abstract
Abstract This paper considers the mean square exponential input-to-state stability (EISS) for stochastic delay reaction-diffusion neural networks (SDRDNNS). SDRDNNS with distributed input and boundary input are investigated. In addition, constant delay and time-varying delay are considered. With the help of Lyapunov-Krasovskii functional method, Ito formula and Wirtinger-type inequality, delay-dependent sufficient conditions on mean square EISS of SDRDNNS are presented. These sufficient conditions show the effects of time-delay and diffusion term on mean-square EISS. Moreover, by means of numerical simulation, the effectiveness of our theoretical results is illustrated.
Journal Title: Neurocomputing
Year Published: 2020
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