A New Looped Functional to Synchronize Neural Networks With Sampled-Data Control
Hong‐Bing Zeng, Zhengliang Zhai, Huaicheng Yan, Wei Wang · IEEE Transactions on Neural Networks and Learning Systems · 2020
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.