Fixed-time neurodynamic optimization approach with time-varying coefficients to variational inequality problems and applications

Xingxing Ju; Xinsong Yang; Shuang Yuan; Daniel W.C. Ho

doi:10.1016/j.cnsns.2024.108414

Fixed-time neurodynamic optimization approach with time-varying coefficients to variational inequality problems and applications

Xingxing Ju, Xinsong Yang^*, Shuang Yuan, Daniel W.C. Ho

^*Corresponding author for this work

Department of Mathematics

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

3 Citations (Scopus)

Abstract

The article presents a novel fixed-time (FT) neurodynamic optimization approach featuring time-varying coefficients, tailored for variational inequality problems (VIPs). This method exhibits noteworthy properties, including FT convergence from any initial point, accelerated by the strategic selection of time-varying coefficients. Detailed upper bounds on the settling time for the time-varying neurodynamic approach are provided. Additionally, the article delves into the robustness of the neurodynamic approach against bounded noise disturbances. Three implementation ways by numerical discretization, analog circuits, and field-programmable gate array (FPGA) of the proposed FT neurodynamic optimization approach are demonstrated. Finally, two applications on Nash equilibrium seeking problems and image recovery are conducted to validate the practicability and superiority of the proposed time-varying neurodynamic approach. © 2024 Elsevier B.V.

Original language	English
Article number	108414
Journal	Communications in Nonlinear Science and Numerical Simulation
Volume	140
Issue number	Part 2
Online published	1 Nov 2024
DOIs	https://doi.org/10.1016/j.cnsns.2024.108414
Publication status	Published - Jan 2025

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 62373262 and 62403336, in part by the Fundamental Research Funds for Central Universities, China under Grant 2023SCU12009, in part by the Sichuan Province Natural Science Foundation of China under Grant 2023NSFSC1433, in part by the Seed Fund Project “Optimal Output Synchronization and Formation Control of Heterogeneous Multi agents under Switching Topology” from Sichuan University and Hong Kong and Macao Universities, in part by the Research Grants Council of Hong Kong under Grant CityU 11213023 and Grant CityU 11205724, in part by the Postdoctoral Fellowship Program (Grade B) of China Postdoctoral Science Foundation under Grant GZB20230467, and in part by the China Postdoctoral Science Foundation under Grant 2023M742457.

Research Keywords

Circuit implementation
Fixed-time convergence
Nash equilibrium seeking
Neurodynamic approaches
Variational inequality problems

RGC Funding Information

RGC-funded

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10.1016/j.cnsns.2024.108414

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Cite this

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title = "Fixed-time neurodynamic optimization approach with time-varying coefficients to variational inequality problems and applications",

abstract = "The article presents a novel fixed-time (FT) neurodynamic optimization approach featuring time-varying coefficients, tailored for variational inequality problems (VIPs). This method exhibits noteworthy properties, including FT convergence from any initial point, accelerated by the strategic selection of time-varying coefficients. Detailed upper bounds on the settling time for the time-varying neurodynamic approach are provided. Additionally, the article delves into the robustness of the neurodynamic approach against bounded noise disturbances. Three implementation ways by numerical discretization, analog circuits, and field-programmable gate array (FPGA) of the proposed FT neurodynamic optimization approach are demonstrated. Finally, two applications on Nash equilibrium seeking problems and image recovery are conducted to validate the practicability and superiority of the proposed time-varying neurodynamic approach. {\textcopyright} 2024 Elsevier B.V.",

keywords = "Circuit implementation, Fixed-time convergence, Nash equilibrium seeking, Neurodynamic approaches, Variational inequality problems",

author = "Xingxing Ju and Xinsong Yang and Shuang Yuan and Ho, {Daniel W.C.}",

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volume = "140",

journal = "Communications in Nonlinear Science and Numerical Simulation",

issn = "1007-5704",

publisher = "Elsevier B.V.",

number = "Part 2",

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Fixed-time neurodynamic optimization approach with time-varying coefficients to variational inequality problems and applications. / Ju, Xingxing; Yang, Xinsong; Yuan, Shuang et al.
In: Communications in Nonlinear Science and Numerical Simulation, Vol. 140, No. Part 2, 108414, 01.2025.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

TY - JOUR

T1 - Fixed-time neurodynamic optimization approach with time-varying coefficients to variational inequality problems and applications

AU - Ju, Xingxing

AU - Yang, Xinsong

AU - Yuan, Shuang

AU - Ho, Daniel W.C.

PY - 2025/1

Y1 - 2025/1

N2 - The article presents a novel fixed-time (FT) neurodynamic optimization approach featuring time-varying coefficients, tailored for variational inequality problems (VIPs). This method exhibits noteworthy properties, including FT convergence from any initial point, accelerated by the strategic selection of time-varying coefficients. Detailed upper bounds on the settling time for the time-varying neurodynamic approach are provided. Additionally, the article delves into the robustness of the neurodynamic approach against bounded noise disturbances. Three implementation ways by numerical discretization, analog circuits, and field-programmable gate array (FPGA) of the proposed FT neurodynamic optimization approach are demonstrated. Finally, two applications on Nash equilibrium seeking problems and image recovery are conducted to validate the practicability and superiority of the proposed time-varying neurodynamic approach. © 2024 Elsevier B.V.

AB - The article presents a novel fixed-time (FT) neurodynamic optimization approach featuring time-varying coefficients, tailored for variational inequality problems (VIPs). This method exhibits noteworthy properties, including FT convergence from any initial point, accelerated by the strategic selection of time-varying coefficients. Detailed upper bounds on the settling time for the time-varying neurodynamic approach are provided. Additionally, the article delves into the robustness of the neurodynamic approach against bounded noise disturbances. Three implementation ways by numerical discretization, analog circuits, and field-programmable gate array (FPGA) of the proposed FT neurodynamic optimization approach are demonstrated. Finally, two applications on Nash equilibrium seeking problems and image recovery are conducted to validate the practicability and superiority of the proposed time-varying neurodynamic approach. © 2024 Elsevier B.V.

KW - Circuit implementation

KW - Fixed-time convergence

KW - Nash equilibrium seeking

KW - Neurodynamic approaches

KW - Variational inequality problems

UR - http://www.scopus.com/inward/record.url?scp=85208196848&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85208196848&origin=recordpage

U2 - 10.1016/j.cnsns.2024.108414

DO - 10.1016/j.cnsns.2024.108414

M3 - RGC 21 - Publication in refereed journal

SN - 1007-5704

VL - 140

JO - Communications in Nonlinear Science and Numerical Simulation

JF - Communications in Nonlinear Science and Numerical Simulation

IS - Part 2

M1 - 108414

ER -

Fixed-time neurodynamic optimization approach with time-varying coefficients to variational inequality problems and applications

Abstract

Funding

Research Keywords

RGC Funding Information

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GRF: Nash Equilibrium Seeking for Multi-Agent Systems with Information Transmission Constraints

GRF: Distributed Mirror Descent Algorithm over Multi-agent Networks with Imperfect Communication

Cite this

Fixed-time neurodynamic optimization approach with time-varying coefficients to variational inequality problems and applications

Abstract

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: Nash Equilibrium Seeking for Multi-Agent Systems with Information Transmission Constraints

GRF: Distributed Mirror Descent Algorithm over Multi-agent Networks with Imperfect Communication

Cite this