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Sampled-data synchronization criteria for Markovian jumping neural networks with additive time-varying delays using new techniques
Applied Mathematics and Computation  (IF4.091),  Pub Date : 2021-09-20, DOI: 10.1016/j.amc.2021.126604
Tao Wu, Jinde Cao, Lianglin Xiong, Haiyang Zhang, Jinlong Shu

This paper investigates the sampled-data synchronization issue of Markovian jumping neural networks with additive time-varying delays. Firstly, a ternary quadratic function negative-determination condition and the bilateral sampled-interval-related Lyapunov functional (BSIRLF) approach are proposed. Based on the developed two novel approaches, some new criteria based on the linear matrix inequalities (LMIs) are established to guarantee the drive-response stochastic sampled-data synchronization of Markovian jumping neural networks with additive time-varying delays. Meanwhile, the corresponding sampled-data controller gains are designed under the larger sampling interval. In the end, the availability and merits of the developed approaches are displayed via two simulative examples.