Finite-Time Synchronization of Coupled Memrisive Neural Network via Robust Control

In this paper, the synchronization of drive-response coupled memristive neural networks (CMNNs) and CMNN with multi-links is investigated. The memristors show the memory characteristics, low energy consumption, and nanometer scale so that CMNN can more truly simulate the working mechanism of brain neural networks. The classic treatment method is no longer being applied because of the parameter-dependent property in CMNN. The new approach is proposed that CMNN is transformed into a class of neural networks with interval parameters under the framework of Filippov solution. This method overcame the problem of mismatched parameters and be less conservative than those existing methods. Sufficient criteria are derived to guarantee the synchronization of the drive-response networks based on the drive-response concept and the Lyapunov function. Finally, the effectiveness of the proposed theories is validated with the numerical experiments.