Predefined-time with time-varying coefficients neurodynamic for composite optimization problems

Dongmei Yu, Shaowei Lin, Gehao Zhang and Hongrui Yin

Chaos, Solitons & Fractals, 2025, vol. 199, issue P3

Abstract: In this paper, we propose a predefined-time with time-varying coefficients neurodynamic (PTTVCN) model to solve composite optimization problems (COPs). We first present the Lyapunov stability conditions for predefined-time stability in time-varying dynamical system and provide specific inferences under different time-varying coefficients. We then propose the PTTVCN model to solve COPs based on the predefined-time stability conditions of time-varying dynamical system. Theoretical analysis verifies that the PTTVCN model can achieve uniform convergence within predefined time and possesses a certain degree of noise resistance. Simulation results are given to show the effectiveness of the proposed predefined-time stability neurodynamic model with time-varying coefficients for COPs. Finally, numerical experiments on both image restoration and Poisson regression problems validate the superiority of the proposed method.

Keywords: Neurodynamic model; Predefined-time stability; Composite optimization problems; Time-varying coefficients (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077925008057
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:199:y:2025:i:p3:s0960077925008057

DOI: 10.1016/j.chaos.2025.116792

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. ().