Smoothing Neural Network for Non-Lipschitz Optimization with Linear Inequality Constraints

YU Xin; WU Lingzhen; XIE Mian; WANG Yanlin; XU Liuming; LU Huixia; XU Chenhua

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Smoothing Neural Network for Non-Lipschitz Optimization with Linear Inequality Constraints

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摘要

This paper presents a smoothing neural network to solve a class of non-Lipschitz optimization problem with linear inequality constraints. The proposed neural network is modelled with a differential inclusion equation, which introduces the smoothing approximate techniques. Under certain conditions, we prove that the trajectory of neural network reaches the feasible region in finite time and stays there thereafter, and that any accumulation point of the solution is a stationary point of the original optimization problem. Furthermore, if all stationary points of the optimization problem are isolated,then the trajectory converges to a stationary point of the optimization problem. Two typical numerical examples are given to verify the effectiveness of the proposed neural network.

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《中国电子杂志（英文版）》 |2021年第4期|634-643|共10页
作者
YU Xin; WU Lingzhen; XIE Mian; WANG Yanlin; XU Liuming; LU Huixia; XU Chenhua;
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作者单位

1. Department of Computer and Electronic Information;

Guangxi University 2. The Guangxi Key Laboratory of Multimedia Communications and Network Technology 3. School of Computer Science and Engineering;

Guilin University of Aerospace Technology 4. Departm;

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正文语种 chi
中图分类人工神经网络与计算;
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Smoothing Neural Network for Non-Lipschitz Optimization with Linear Inequality Constraints

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