An accelerated neural dynamics model for solving dynamic nonlinear optimization problem and its applications

Dongyang Fu, Yang Si, Difeng Wang and Yizhen Xiong

Chaos, Solitons & Fractals, 2024, vol. 180, issue C

Abstract: Zeroing neural dynamics (ZND) model is a powerful tool for solving dynamic problems. This study presents an accelerated neural dynamics (AND) model by solving a dynamic nonlinear optimization (DNO) problem. Different from the classical activation function (AF), the AND model describes a novel accelerated convergence strategy that designs a nonlinear dynamic variable according to the error paradigm. Additionally, the AND model can be converted into a paradigm-based dynamical mode, which provides a quantification of the convergence time. Notably, the AND model shows outstanding robustness to various perturbations in the computational environment. The superiority of the AND model is further validated by comparing different models. Subsequently, the model’s practicality is shown through the utilization of acoustic-based time difference of arrival (TDOA) localization.

Keywords: Dynamic nonlinear optimization; Accelerated neural dynamics (AND); Acoustic localization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077924000936
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:180:y:2024:i:c:s0960077924000936

DOI: 10.1016/j.chaos.2024.114542

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. ().