Neurodynamic optimization approaches with finite/fixed-time convergence for absolute value equations

Xingxing Ju, Xinsong Yang*, Gang Feng, Hangjun Che

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

16 Citations (Scopus)

Abstract

This paper proposes three novel accelerated inverse-free neurodynamic approaches to solve absolute value equations (AVEs). The first two are finite-time converging approaches and the third one is a fixed-time converging approach. It is shown that the proposed first two neurodynamic approaches converge to the solution of the concerned AVEs in a finite-time while, under some mild conditions, the third one converges to the solution in a fixed-time. It is also shown that the settling time for the proposed fixed-time converging approach has an uniform upper bound for all initial conditions, while the settling times for the proposed finite-time converging approaches are dependent on initial conditions. The proposed neurodynamic approaches have the advantage that they are all robust against bounded vanishing perturbations. The theoretical results are validated by means of a numerical example and an application in boundary value problems. © 2023 Elsevier Ltd.
Original languageEnglish
Pages (from-to)971-981
JournalNeural Networks
Volume165
Online published3 Jul 2023
DOIs
Publication statusPublished - Aug 2023

Research Keywords

  • Absolute value equations
  • Finite-time convergence
  • Fixed-time convergence
  • Neurodynamic approaches
  • Robustness

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