Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control

Neural Networks : the Official Journal of the International Neural Network Society
Wei ZhangMingqing Xiao

Abstract

In this paper, the synchronization problem for neural networks with stochastic perturbation is studied with intermittent control via adaptive aperiodicity. Under the framework of stochastic theory and Lyapunov stability method, we develop some techniques of intermittent control with adaptive aperiodicity to achieve the synchronization of a class of neural networks, modeled by stochastic systems. Some effective sufficient conditions are established for the realization of synchronization of the underlying network. Numerical simulations of two examples are provided to illustrate the theoretical results obtained in the paper.

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Citations

May 23, 2019·Neural Computation·Yong YuJianxun Zhang
May 24, 2020·Neural Networks : the Official Journal of the International Neural Network Society·Deqiang OuyangHeng Tao Shen
Jan 23, 2017·Neural Networks : the Official Journal of the International Neural Network Society·Leimin WangXiaoping Wang

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