Global Mittag-Leffler synchronization of discrete-time fractional-order neural networks with time delays

Xiao-Li Zhang, Hong-Li Li, Yonggui Kao, Long Zhang and Haijun Jiang

Applied Mathematics and Computation, 2022, vol. 433, issue C

Abstract: In this article, the problem of the global Mittag-Leffler synchronization is proposed for a sort of discrete-time fractional-order neural networks (DFNNs) with delays. In the first place, a flesh power law inequality pertaining to fractional difference is constructed by means of integration by parts, Young inequality, and some properties about fractional-order difference. In addition, based on aforesaid inequalities, Lyapunov function theory and properties of nabla Mittag-Leffler function as well as inequality techniques, some plentiful criteria are formed to achieve the global Mittag-Leffler synchronization for the delayed DFNNs via devising novel adaptive controller and delay feedback controller. In the end, numerical modeling is given to demonstrate effectiveness of theoretical verdicts.

Keywords: Discrete-time; Fractional-order neural networks; Mittag-Leffler synchronization; Time delays; Adaptive control (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:433:y:2022:i:c:s009630032200491x

DOI: 10.1016/j.amc.2022.127417

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