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The finite-time synchronization problem of stochastic memristor-based delayed neural network is studied. Certain sufficient conditions are got to assure finite-time synchronization of the chaotic stochastic memristor-based neural networks by using differential inclusions theory, finite-time stability theorem, Lyapunov functional, inequality techniques, stochastic analysis theory and designing a suitable controller. Comparison with previous results, the model of memristor-based neural network of this paper is general, and the given stability conditions are novel. Therefore, the obtained results generalize and improve some existing achievements about the memristor-based neural network. Moreover, a numerical simulation example demonstrates the usefulness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, finite-time synchronization (FTS) of memristor-based recurrent neural networks (MNNs) with time-varying delays is investigated by designing a new switching controller. First, by using the differential inclusions theory and set-valued maps, sufficient conditions to ensure FTS of MNNs are obtained under the two cases of 0 < α < 1 and α = 0 , and it is derived that α = 0 is the critical value of 0 < α < 1 . Next, it is discussed deeply on the relation between the parameter α and the synchronization time. Then, a new controller with a switching parameter α is designed which can shorten the synchronization time. Finally, some numerical simulation examples are provided to illustrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper investigates exponential synchronization of networked dynamical systems under event-triggered control schemes. Two event-triggered sampled-data transmission schemes, which only need the latest observations of their neighborhood and the virtual leader to predict the next observation time, are designed to realize exponential synchronization of networked dynamical systems. That is, the coupled information is updated only when the triggered conditions are violated. Hence, continuous communication can be avoided and the number of information transmission is reduced. A positive lower bound for inter-event intervals is achieved to exclude Zeno behavior. Finally, two numerical simulation examples are provided to illustrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, a feedback controller is proposed for the synchronization of memristive competitive neural networks with different time scales. By constructing a proper Lyapunov-Krasovskii functional, as well as employing differential inclusions theory, a feedback controller is designed to achieve the asymptotical synchronization of coupled competitive neural networks. The proposed synchronization algorithm is simple and can be easily realized. A simulation example is given to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an integral sliding mode control (SMC) approach is presented to investigate the projective synchronization of nonidentical chaotic neural networks with mixed time delays. By considering a proper sliding surface and constructing Lyapunov–Krasovskii functional, as well as using the linear matrix inequality (LMI) technique, a sliding mode controller is designed to achieve the projective synchronization of the different neural networks. Finally, numerical simulations are carried out to illustrate the effectiveness of the main results. [Copyright &y& Elsevier]