Exponentially finite-time dissipative discrete state estimator for delayed competitive neural networks via semi-discretization approach

B. Adhira and G. Nagamani

Chaos, Solitons & Fractals, 2023, vol. 176, issue C

Abstract: This article is concerned with the investigation of finite-time boundedness and exponential (Q,S,R)-dissipative performance for a class of discretized competitive neural networks (CNNs) with time-varying delays. Initially, by employing the semi-discretization technique, a discrete analog of the continuous-time CNNs is formulated, which preserves the dynamical behaviors of their continuous-time counterpart. An appropriate state estimator is developed for the discretized CNNs so that the dynamics of the associated estimation error system attain finite-time exponential (Q,S,R)-dissipative performance. Further, to obtain a tighter summation bound, two novel weighted summation inequalities are proposed, which linearize the quadratic summable terms occurring in the finite difference of the considered Lyapunov–Krasovskii functional. Finally, to refine our prediction, an illustrative example is provided that demonstrates the sustainability and merits of the proposed method.

Keywords: Competitive neural networks; Finite-time boundedness property; Dissipative performance measure; Semi-discretization approach; Linear matrix inequality (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077923010640
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:176:y:2023:i:c:s0960077923010640

DOI: 10.1016/j.chaos.2023.114162

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. ().