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This paper investigates the globally exponential stability of switched neural networks with time-varying delays. By virtue of mode-dependent average dwell time, some significant criteria of exponential stability are obtained for delayed switched neural networks with only stable subsystems or with both stable subsystems and unstable subsystems. The proposed theoretical results could be utilized not only to verify the globally exponential stability of switched neural networks, but also to design appropriate switching signal to guarantee the globally exponential stabilization. They can explicitly reflect the effect of mode-dependent average dwell time on the stability of switched neural networks. In contrast to the previous results, the proposed criteria are more straightforward and effective in the real-world application. Three numerical examples are introduced to illustrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential synchronization of chaotic systems with time delays via periodically intermittent control. Under the new differential inequality, some novel synchronization criteria are derived. In contrast to the existing works, the proposed results are less conservative because they can obtain more precise synchronized rate under the identical control conditions and remove the restrictions on the control period (or the control width) and the time delay. By using special parameters, the feasible region D(ξ), which guarantees the response system synchronizes with the drive system with synchronized rate 0.5ξ, is obtained. The Lu chaotic attractor and a first-order chaotic system with time delay are presented to demonstrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper we investigate the stability of neural networks with time-delay and variable-time impulses. A valid approach on calculating the upper bound and lower bound of dwell time is proposed, which is crucial contribution of this paper comparing to previous results on the stability of neural networks with variable-time impulses. Some reasonable sufficient conditions on guaranteeing the global stability of variable-time impulsive neural networks are introduced. These results are also generalized to the case of fixed-time impulse by taking neural networks with fixed-time impulses as the specific form of variable-time impulsive neural networks. Finally, numerical examples are also given to illustrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]