Almost Sure Asymptotical Adaptive Synchronization for Neutral‐Type Neural Networks with Stochastic Perturbation and Markovian Switching
Wuneng Zhou, Xueqing Yang, Jun Li Yang, Anding Dai, Huashan Liu · Mathematical Problems in Engineering · 2014
The problem of almost sure (a.s.) asymptotic adaptive synchronization for neutral‐type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral‐type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral‐type neural networks with stochastic perturbation and Markovian switching. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper.