Exponential Synchronization for Semi-Markovian Delayed Memristive Neural Networks with Intermittent Sampled-Data Control

This paper studies the problem of Exponential Synchronization (PES) for Memristor-based Neural Networks (MNNs) with Time-varying Delayed (TD) and semi-Markov Jump Parameters (semi-MJPs), employing Intermittent Sampled-data control (ISDC). By exploiting the free-matrix exponential-type inequality in conjunction with other analytical methodologies, a novel criterion for the stochastic exponential stability (CSES) of the error system is established, rooted in the LKF approach. This newly obtained criterion is expressed in terms of linear matrix inequalities, thereby alleviating the computational load associated with nonlinear matrix inequalities. Finally, present a numerical example that demonstrates the effectiveness of the proposed result.