Work

The prescribed-time neurodynamic approaches for solving time-varying absolute value equations

Year: 2026

Type: article

Abstract: In this paper, we consider time-varying absolute value equations (TVAVEs). Incorporating the advantages of zeroing neurodynamic approaches (ZNA) and prescribed-time convergence, a novel prescribed-time robust zeroing neurodynamic approach (PTRZNA) is proposed for solving TVAVEs. Through rigorous theoretical analysis, it is proved that PTRZNA can not only tackle with TVAVEs in prescribed time, but also is independent of the initial conditions. In addition, PTRZNA is tolerant to any bounded noise. By introducing adaptive parameters and robust design, the prescribed-time gradient neurodynamic approach (PTGNA) is proposed. Theoretical analysis indicates that PTGNA can achieve convergence within the prescribed time and is robust to bounded vanishing noise and bounded non-vanishing noise. Furthermore, PTGNA can effectively solve TVAVEs in an inverse-free manner. Numerical simulations are given to demonstrate the effectiveness of the proposed PTRZNA and PTGNA.

Source: Chaos Solitons & Fractals

DOI: 10.1016/j.chaos.2026.117994

Authors: Dongmei Yu, Tiange Ma, Gehao Zhang

Institution: Liaoning Technical University

Language: English

Topic: Optimization and Variational Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,970

Funders: Natural Science Foundation of Liaoning Province, Department of Education of Liaoning Province, Scientific Research Fund of Liaoning Provincial Education Department