Finite-Time Stabilization of Memristor Neural Networks with Time-Varying Delay: Interval Matrix Method

In this paper, the finite-time stabilization problem for a class of memristor neural networks (MNNs) with time-varying delay is investigated. In order to ensure the finite-time stability of MNNs with time-varying delay, the general conditions of the feedback control laws are given. To deal with the parameter jump of MNNs with time-varying delay and different from previous research methods, MNNs with time-varying delay are transformed into a class of time-varying delay systems with interval parameters based on the interval matrix method, which reduces the conservativeness of the systems. Several sufficient conditions to guarantee the finite-time stabilization of MNNs are obtained by the linear matrix inequalities (LMIs). In addition, the upper bound of the settling time is estimated. Finally, a numerical simulation is carried out to verify the correctness and validity of the results.