title: Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control.
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.
authors: College of Computer Science, Chongqing University, Chongqing 400044, PR China. Electronic address: cquzw@hotmail.com.
authors: Wei
authors: W
authors: Zhang
authors: College of Computer Science, Chongqing University, Chongqing 400044, PR China. Electronic address: licd@cqu.edu.cn.
authors: Chuandong
authors: C
authors: Li
authors: Department of Mathematics, Texas A&M University at Qatar, Doha, P.O.Box 23874, Qatar. Electronic address: tingwen.huang@qatar.tamu.edu.
authors: Tingwen
authors: T
authors: Huang
authors: Department of Mathematics, Southern Illinois University, IL 62901, USA. Electronic address: mxiao@siu.edu.
authors: Mingqing
authors: M
authors: Xiao
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D013269:Stochastic Processes
keywords: Adaptive control; Aperiodically intermittent control; Neural networks; Stochastic perturbation; Synchronization