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Neural Networks
Article . 2023 . Peer-reviewed
License: Elsevier TDM
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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
zbMATH Open
Article . 2023
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https://doi.org/10.2139/ssrn.4...
Article . 2023 . Peer-reviewed
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Neurodynamic Optimization Approaches with Finite/Fixed-Time Convergence for Absolute Value Equations

Neurodynamic optimization approaches with finite/fixed-time convergence for absolute value equations
Authors: Xingxing Ju; Xinsong Yang; Gang Feng 0001; Hangjun Che;

Neurodynamic Optimization Approaches with Finite/Fixed-Time Convergence for Absolute Value Equations

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.

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Keywords

Numerical methods for differential-algebraic equations, Convex programming, Numerical optimization and variational techniques, neurodynamic approaches, finite-time convergence, fixed-time convergence, absolute value equations, robustness, Neural Networks, Computer

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
18
Top 10%
Top 10%
Top 10%
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