Memory-based switched event-triggered stabilization with switched gains for memristive neural networks

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Abstract

This paper investigates the problem of event-triggered stabilization for memristive neural networks (MNNs). To reduce the consumption of limited network communication resources, a memory-based switched event-triggered rule is proposed, in which an integral-type time-memory function is incorporated into the triggering threshold to characterize the average historical state information. The proposed event-triggered rule divides the entire time interval into a variable-length waiting interval and a continuous, event-triggered interval. To enlarge the maximum allowable variable-length waiting interval, a switched controller with different feedback gains is designed for these two intervals. A sufficient condition ensuring asymptotic stability of the closed-loop MNN is derived by constructing a piecewise Lyapunov functional, employing several integral inequalities, and introducing appropriate slack matrices. Subsequently, by decoupling the nonlinearities, a convex matrix-inequalities-based condition is established, enabling the explicit computation of the feedback gains. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed memory-based switched event-triggered stabilization scheme.

Keywords

memristive neural network / switched gain / asymptotic stability / event-triggered control

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