Finite-time synchronization of memristor neural networks via interval matrix method

In this paper, the finite-time synchronization problems of two types of driven-response memristor neural networks (MNNs) without time-delay and with time-varying delays are investigated via interval matrix method, respectively. Based on interval matrix transformation, the driven-response MNNs are transformed into a kind of system with interval parameters, which is different from the previous research approaches. Several sufficient conditions in terms of linear matrix inequalities (LMIs) are driven to guarantee finite-time synchronization for MNNs. Correspondingly, two types of nonlinear feedback controllers are designed. Meanwhile, the upper-bounded of the settling time functions are estimated. Finally, two numerical examples with simulations are given to illustrate the correctness of the theoretical results and the effectiveness of the proposed controllers. •The MNNs without time-delay and with time-varying delays are transformed into a kind of system with interval parameters, which overcomes the influence of switching jumping parameters.•Two different kinds of nonlinear feedback controllers are constructed by solving several LMIs, which is independent and dependent on time-delay, respectively.•The interval matrix method is used to investigate the finite-time synchronization of MNNs, which reduces conservativeness and easily constructs solvable nonlinear finite-time synchronization controllers.