Sampled-Data Synchronization Control for Chaotic Neural Networks With Mixed Delays: A Discontinuous Lyapunov Functional Approach

The sampled-data synchronization problem of chaotic neural networks with mixed time delays is investigated in this paper. A novel augmented discontinuous Lyapunov-Krasovskii functional (ADLKF) is constructed, which can make full capture the more information of delay variation and the characteristics about the actual sampling information. Based on the ADLKF and modified free-matrix-based integral inequality, novel less conservative synchronization criteria are developed to guarantee that the slave system is synchronous with the master system. Furthermore, the derived results are given in terms of simplified linear matrix inequalities (LMIs), which can be straightforwardly solved by Matlab. Finally, two numerical examples are proposed to demonstrate the effectiveness and the benefits of the presented synchronization scheme.