New Approximate Results of Fixed-Time Stabilization for Delayed Inertial Memristive Neural Networks
Guodong Zhang, Shiping Wen
Abstract
This article discusses fixed-time stabilization of inertial memristive neural networks with mixed-time delays. First, new approximate results on settling-time of fixed-time stable are constructed, which are more accurate than the ones given in earlier works. Then, based on the new approximate results, some effective criteria are derived to achieve fixed-time stabilization with event-triggered control. Unlike reduced-order approach, this article through nonreduced-order way to investigate such system that enhances directness of the derived results. In the end, some simulations and comparisons are listed to show the validity and advantage of the new proposed approximate settling-time and criteria of fixed-time stabilization.