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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Pages (from-to)971-981
Journal / PublicationNeural Networks
Online published3 Jul 2023
Publication statusPublished - Aug 2023


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.

Research Area(s)

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