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This article mainly concentrates on the synchronization problem for a more general kind of the master–slave memristor-based neural networks with fractional derivative. By applying a continuous-frequency-distributed equivalent model tool, some new outcomes and sufficient conditions on the robust synchronization of the master–slave neural networks with uncertainty are proposed via linear matrix inequality (LMI). Finally, two memristive neural networks model with fractional derivatives are presented to validate the efficiency of the theoretical results. [ABSTRACT FROM AUTHOR]

The time-triggered impulsive control of complex homogeneous dynamical networks has received wide attention due to its occasional occupation of the communication channels. This article is devoted to quasisynchronization of heterogeneous dynamical networks via event-triggered impulsive controls with less channel occupation. Two kinds of triggered mechanisms, that is, the centralized event-triggered mechanism in which the control is updated based upon the state information of all nodes, and the distributed event-triggered mechanism where the control is updated according to the state information of each node and its neighboring node, are proposed, respectively, such that the synchronization error between the heterogeneous dynamical networks and a virtual target is not more than a nonzero bound. What is more, the Zeno behavior is shown to be excluded. It is found that the combination method of the event-triggered control and the impulsive control, that is, the distributed event-triggered impulsive control has the advantage of low-energy consumption and takes up many fewer communication channels over the time-triggered impulsive control. Two numerical examples are conducted to illustrate the effectiveness of the proposed event-triggered impulsive controls. [ABSTRACT FROM AUTHOR]

This article deals with the exponential synchronization problem for complex dynamical networks (CDNs) with coupling delay by means of the event-triggered delayed impulsive control (ETDIC) strategy. This novel ETDIC strategy combining delayed impulsive control with the event-triggering mechanism is formulated based on the quadratic Lyapunov function. Among them, the event-triggering instants are generated whenever the ETDIC strategy is violated and delayed impulsive control is implemented only at event-triggering instants, which allows the existence of some network problems, such as packet loss, misordering, and retransmission. By employing the Lyapunov–Razumikhin (L–R) technique and impulsive control theory, some sufficient conditions with less conservatism are proposed in terms of linear matrix inequalities (LMIs), which indicates that the ETDIC strategy can guarantee the achievement of the exponential synchronization and eliminate the Zeno phenomenon. Finally, a numerical example and its simulations are presented to verify the effectiveness of the proposed ETDIC strategy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel class of networks named as quaternion-valued inertial memristor-based neural networks (QVIMNNs) by introducing inertial term and memristor into traditional quaternion-valued neural networks (QVNNs). The problem of fixed-time synchronization of the QVIMNNs is investigated based on the variable transformation and Lyapunov functional method. It is shown that two types of activation functions are considered and a novel criteria guaranteeing fixed-time synchronization for each cases are then achieved by designing different types of controllers. This paper attempts to pave a new way to investigate neural networks classes with numerical simulations support to demonstrate the correctness of the obtained results. [ABSTRACT FROM AUTHOR]

Reference 1 Song C., Cao J., Abdel-Aty M. New Results on Robust Synchronization for Memristive Neural Networks with Fractional Derivatives via Linear Matrix Inequality. New Results on Robust Synchronization for Memristive Neural Networks with Fractional Derivatives via Linear Matrix Inequality. In the original publication, there were two mistakes in Figure 1 and Figure 2, as published [[1]]. [Extracted from the article]

This paper is concerned with the cluster synchronization of stochastic neural networks with delay. By pinning selected nodes of stochastic network at impulse time, an impulsive strategy is proposed and two methods of cluster decay in the mean square are derived. In order to overcome the effect of impulse and the restriction of time delay, stochastic impulsive analysis technique is introduced and the estimation of cluster decay of neural networks can be obtained by comparing to a linear discrete system. In addition, numerical example is given to demonstrate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]