Mingwen Zheng

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.

Parameters estimation and synchronization of uncertain coupling recurrent dynamical neural networks with time-varying delays based on adaptive control

This paper mainly studies the parameters estimation and synchronization of coupling recurrent dynamical neural networks (CRDNNs). Here, the weights and coupling parameters of CRDNNs are unknown. By designing a new adaptive controller and parameters update rules, a simple linear matrix inequality criterion is established for the parameters estimation and synchronization problem of CRDNNs. We prove the conclusion by constructing a suitable Lyapunov function. A numerical simulation is given to show the effectiveness of the conclusion.

Fixed-time synchronization of memristor-based fuzzy cellular neural network with time-varying delay

Abstract This paper mainly investigates the fixed-time synchronization of memristor-based fuzzy cellular neural network (MFCNN) with time-varying delay. By utilizing differential inclusion, set-valued map theory, the definitions of finite-time and fixed-time stability, we convert the fixed-time synchronization control of the drive-response MFCNN into the equivalent fixed-time stability problem of the error system between the drive-response systems. Some novel sufficient conditions are derived to guarantee the fixed-time synchronization of the drive-response MFCNN based on a simple Lyapunov function and a nonlinear feedback controller. Meanwhile, the settling time can be estimated by simple calculations. Furthermore, these fixed-time synchronization criteria here are easy to validate and extend to the MFCNN without time-varying delay and general memristor-based neural networks. Finally, three numerical examples are given to illustrate the correctness of the main results.