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In this paper, we study the fixed-time synchronization (FIXTS) of neural networks (NNs) with parameter uncertainties via quantized intermittent control. Based on the intermittent control strategy and quantitative control theory, sufficient conditions are established to achieve synchronization of NNs and the synchronization time can be estimated. In addition, this paper also considers the synchronization of NNs under different situations. Finally, a simulation example is given to verify the correctness of the proposed theory. [ABSTRACT FROM AUTHOR]

This paper investigates finite-time synchronization of complexed-valued neural networks with multiple time-varying delays and infinite distributed delays. By separating the complex-valued neural networks into the real and the imaginary parts, the corresponding equivalent real-valued systems are obtained. Some sufficient conditions are derived for finite-time synchronization of the drive-response system based on the new Lyapunov–Krasovskii function and the new analysis techniques. Numerical examples demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper aims at analyzing the impulsive synchronization of fractional-order neural works. Firstly, in view of control theory, by constructing a suitable impulsive response system with the designed controller, the synchronization error system between the drive system and the corresponding response system is given. Afterwards, based on the theory of impulsive differential equation, the theory of fractional differential equation, Lyapunov direct method, and inequality techniques, some effective sufficient criteria are established to guarantee the global Mittag-Leffler stability for the synchronization error system. Finally, several simulation examples are designed to demonstrate the effectiveness and feasibility of the obtained results. [ABSTRACT FROM AUTHOR]

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]

This paper studies projective lag synchronization of coupled neural networks with time delay and parameter mismatch. An adaptive controller is designed to achieve weak projective lag synchronization of coupled neural networks. This method is employed to realize projective lag synchronization between coupled neural systems with an error level. Numerical simulation illustrates the effectiveness of the results. [ABSTRACT FROM AUTHOR]