Stabilization of memristive neural networks with mixed time-varying delays via continuous/periodic event-based control

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Journal Article
Journal of the Franklin Institute, 2020, 357, (11), pp. 7122-7138
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This paper addresses the asymptotic stabilization of memristive neural networks with mixed time-varying delays. With two different sampling schemes, sufficient conditions for asymptotic stability of the delayed memristive neural networks system can be obtained by designing appropriate event-based controllers. It is worth mentioning that the state-dependent memristive neural network model in this paper includes time-varying discrete and distributed delays, which is a generalization of the traditional neural network model. Furthermore, based on the continuous sampling event trigger control scheme, a method for designing more economical periodic sampling event trigger control scheme is proposed. Finally, to verify the validity of our conclusions, two numerical simulation examples are given.
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