Complete synchronization for discrete-time fractional-order coupled neural networks with time delays

Xueke Cui, Hong-Li Li, Long Zhang, Cheng Hu and Haibo Bao

Chaos, Solitons & Fractals, 2023, vol. 174, issue C

Abstract: This paper is devoted to investigating complete synchronization for a class of discrete-time fractional-order coupled neural networks (DFCNNs) with time delays, which has not been documented yet. For the sake of answering the challenging problem mentioned above, we generalize the classic Barbalat’s lemma to discrete-time fractional-order (DF) version. After that a novel hybrid controller composed of adaptive control term and delayed feedback control term is firstly designed to reach synchronization of DFCNNs with time delays. Then, several well-done synchronization criteria are acquired by the utilization of fractional calculus theory, DF Barbalat’s lemma and inequality techniques. In the end, a numerical example is put forward to exemplify the validity of our results.

Keywords: Discrete-time; Fractional-order coupled neural networks; Time delays; Barbalat’s lemma; Hybrid controller (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077923006732
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923006732

DOI: 10.1016/j.chaos.2023.113772

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().