LAUSR

This paper investigates the sampled-data-based ${H_\infty }$ synchronization problem for a class of switched coupled neural networks subject to exogenous perturbations. Different from the existing results on the nonswitched and continuous-time control cases, the unmatched phenomena between the switching of the system models and that of the controllers will occur, when the resulting error system switches within a sampling interval. In the framework of time-dependent switching mechanism, sufficient conditions for the existence of the sampled-data controllers are derived under the variable sampling and asynchronous switching. We prove that the proposed method not only renders the synchronization error system exponentially stable but also constrains the influence of the exogenous perturbations on the synchronization performance at a specified level. Finally, a switched coupled cellular neural network and a switched coupled Hopfield neural network are provided to illustrate the applicability and validity of the developed results.