Fixed-time Synchronization Control of Memristive Neural Networks

The memristive neural network is a new type of artificial neural network. Unlike traditional neural network, it replaces conventional weight resistors with memristors. This makes it increasingly useful in fields such as encrypted communication and information classification. This paper studies the synchronization control problem for the memristive neural networks and presents a design method of fixed-time synchronization controller. First, the error dynamic system is obtained by combining the drive system and the response system. Second, a nonlinear controller is designed based on the error system. Under the action of the designed controller, the error system converges to the origin within a fixed time. It enables the fixed-time synchronization of drive system and response system to be achieved. Finally, the numerical simulation experiment results demonstrate the effectiveness of the designed synchronous controller.