复合优化问题的时变系数预定义时间神经动力学

IF 5.3 1区数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Chaos Solitons & Fractals Pub Date : 2025-07-15 DOI:10.1016/j.chaos.2025.116792

Dongmei Yu , Shaowei Lin , Gehao Zhang , Hongrui Yin

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引用次数: 0

摘要

本文提出了一种具有时变系数的预定义时间神经动力学（PTTVCN）模型来解决复合优化问题（cop）。首先给出了时变动力系统预定义时稳定性的Lyapunov稳定性条件，并给出了不同时变系数下的具体推论。在此基础上，提出了基于时变动力系统时间稳定性条件的PTTVCN模型。理论分析验证了PTTVCN模型能在预定时间内实现均匀收敛，并具有一定的抗噪性。仿真结果表明，所提出的带时变系数的预定义时间稳定性神经动力学模型是有效的。最后，对图像恢复和泊松回归问题进行了数值实验，验证了该方法的优越性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predefined-time with time-varying coefficients neurodynamic for composite optimization problems

In this paper, we propose a predefined-time with time-varying coefficients neurodynamic (PTTVCN) model to solve composite optimization problems (COPs). We first present the Lyapunov stability conditions for predefined-time stability in time-varying dynamical system and provide specific inferences under different time-varying coefficients. We then propose the PTTVCN model to solve COPs based on the predefined-time stability conditions of time-varying dynamical system. Theoretical analysis verifies that the PTTVCN model can achieve uniform convergence within predefined time and possesses a certain degree of noise resistance. Simulation results are given to show the effectiveness of the proposed predefined-time stability neurodynamic model with time-varying coefficients for COPs. Finally, numerical experiments on both image restoration and Poisson regression problems validate the superiority of the proposed method.

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来源期刊

Chaos Solitons & Fractals 物理-数学跨学科应用

CiteScore

13.20

自引率

10.30%

发文量

1087

审稿时长

9 months

期刊介绍： Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.