基于 HP 增强拉格朗日函数的分布式非凸优化神经动力优化方法。

IF 6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2024-10-11 DOI:10.1016/j.neunet.2024.106791

Huimin Guan , Yang Liu , Kit Ian Kou , Weihua Gui

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引用次数: 0

摘要

本文建立了分布式非凸约束优化的神经动力学模型。在分布式约束优化模型中，目标函数和不等式约束不需要是凸的，等式约束不需要是仿射的。建立了一个用于处理非凸性的 Hestenes-Powell 增强拉格朗日函数，并在此基础上开发了一个神经动力系统。研究证明，该系统在优化模型的局部最优解处是稳定的。本文提供了两个示例来评估所开发的神经动力系统的稳定性和最优性。

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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A neurodynamic optimization approach to distributed nonconvex optimization based on an HP augmented Lagrangian function

This paper develops a neurodynamic model for distributed nonconvex-constrained optimization. In the distributed constrained optimization model, the objective function and inequality constraints do not need to be convex, and equality constraints do not need to be affine. A Hestenes–Powell augmented Lagrangian function for handling the nonconvexity is established, and a neurodynamic system is developed based on this. It is proved that it is stable at a local optimal solution of the optimization model. Two illustrative examples are provided to evaluate the enhanced stability and optimality of the developed neurodynamic systems.

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来源期刊

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.