Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4948002 | Neurocomputing | 2017 | 45 Pages |
Abstract
In this paper, the intermittent Hâ synchronization problem for a class of reaction-diffusion neural networks with Dirichlet boundary conditions is investigated. Different from the previous works concerning intermittent synchronization, it is assumed that the response system is subject to external disturbance, and both the control period and the control width may be variable. A switching-time-dependent Lyapunov function combined with the use of the extended Wiritinger's inequality for exploring the stabilizing role of the reaction-diffusion term is employed to analyze the exponential stability and L2-gain performance of the synchronization error dynamics. In the framework of linear matrix inequalities, the aperiodically intermittent Hâ synchronization controller is designed, which guarantees internally exponential stability as well as a prescribed L2-gain from the exogenous input to the regulated error output. Two numerical examples are given to illustrate the effectiveness of the proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Lijun Liu, Wu-Hua Chen, Xiaomei Lu,