A New Looped Functional to Synchronize Neural Networks With Sampled-Data Control

This article deals with the problem of sampled-data-based synchronization of neural networks with and without considering time delay. A novel looped functional is introduced in the construction of Lyapunov functional, which adequately utilizes the state information of e(t_{k}) , e(t) , e(t_{k+1}) , e(t_{k}-{\tau _{c}}) , e(t-{\tau _{c}}) , and e(t_{k+1}-{\tau _{c}}) . Then, by using this functional and employing a generalized free-matrix-based integral inequality (GFMBII), several sufficient conditions are derived to ensure that the slave system is synchronous with the master system. Also, the sampled-data controller can be obtained by using the linear matrix inequality (LMI) technique. Finally, two numerical examples are illustrated to show the validity and advantages of the proposed method.