Finite-Time Synchronization of Memristive Neural Networks with Uncertainties and External Disturbances

An improved synchronization approach is proposed, designed and proved by employing SMC technique and wavelet Hopfield neural networks. Firstly, a novel memristive celluar neural network (MCNN) is introduced and its dynamics are analyzed. Then, based on Lyapunov theory, The sliding mode controllers are also designed to robust the uncertain mode and the adaptive update laws are derived so that the synchronization error of MCNNs with uncertainties and external disturbances can be reached zero in finite time by using SMC with activation function. Finally, taking the proposed MCNN as an example, the efficacy of proposed method is validated.