Synchronization of reaction–diffusion neural networks with sampled-data control via a new two-sided looped-functional

Mingchen Huan and Chuandong Li

Chaos, Solitons & Fractals, 2023, vol. 167, issue C

Abstract: In this paper, the exponential synchronization problem of reaction–diffusion neural networks with sampled-data control is addressed via looped-functional approach. Considering the transmission delay of sampled-data controller, a two-sided looped-functional is designed to obtain the synchronization conditions, which are less conservative than those with the traditional Lyapunov method. The research results are applied to Takagi–Sugeno (T–S) fuzzy models with reaction–diffusion terms. Three numerical examples are presented to show the feasibility and effectiveness of our methods.

Keywords: Exponential synchronization; Reaction–diffusion neural networks; T–S fuzzy model; Sampled-data control; Looped-functional (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/S0960077922012383
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:167:y:2023:i:c:s0960077922012383

DOI: 10.1016/j.chaos.2022.113059

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. ().