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This paper investigates the stabilizing and synchronization problems of a memristor-based Chua chaotic system in a finite time. A lemma concerning the finite-time stability for an impulsive system is proposed by extending the finite-time stability theory. Then some finite-time stabilizing and synchronization criterion are presented which guarantee the finite-time stabilization and synchronization for the model considered. Finally, the efficiency of the control scheme is further demonstrated by the simulation examples. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a finite-time lag synchronization of coupled neural networks with time delay is investigated. By means of the Lyapunov stability theory, a feedback controller is designed for achieving lag synchronization between two delayed neural networks systems in finite time. Paper extends some basic results from the area of finite time to time-delay systems. Numerical simulations on coupled Lu neural systems illustrate the effectiveness of the results. [Copyright &y& Elsevier]

This paper investigates the fixed-time synchronization of complex networks (CNs) with nonidentical nodes and stochastic noise perturbations. By designing new controllers, constructing Lyapunov functions and using the properties of Weiner process, different synchronization criteria are derived according to whether the node systems in the CNs or the goal system satisfies the corresponding conditions. Moreover, the role of the designed controllers is analyzed in great detail by constructing a suitable comparison system and a new method is presented to estimate the settling time by utilizing the comparison system. Results of this paper can be applied to both directed and undirected weighted networks. Numerical simulations are offered to verify the effectiveness of our new results. [ABSTRACT FROM AUTHOR]

This paper focuses on the hybrid effects of coupling, time-varying delay, intermittent controller on memristor-based neural networks. Firstly, we constructed an array of linearly coupled memristor-based neural networks with time-varying delay. Secondly, our sufficient conditions, ensuring the stability and synchronization, are dependent on coupling and intermittent control and show coupling and intermittent control on the stability and synchronization of memristor-based neural networks. Finally, numerical simulations were given to verify the theoretical results. [ABSTRACT FROM AUTHOR]