巢湖学院学报 ›› 2023, Vol. 25 ›› Issue (6): 60-66.doi: 10.12152/j.issn.1672-2868.2023.06.008

• 数理科学 • 上一篇    下一篇

矩阵型投影神经网络模型解混合约束非线性优化

叶甜甜,陈佩树,费经泰:巢湖学院 数学与大数据学院   

  1. 巢湖学院 数学与大数据学院,安徽 巢湖 238024
  • 收稿日期:2023-03-04 出版日期:2023-11-25 发布日期:2024-05-27
  • 作者简介:叶甜甜(1995—),女,安徽安庆人,巢湖学院数学与大数据学院助教,主要从事智能计算研究。
  • 基金资助:
    巢湖学院高水平科研成果培育项目(项目编号:kj20zkjp04);巢湖学院重点建设学科项目(项目编号:kj22zdjsxk01);巢湖学院自然科学研究一般项目(项目编号:XLY-202201)

A Matrix Projection Neural Network Model for Solving Mixed Constrained Nonlinear Optimization

YE Tian-tian,CHEN Pei-shu,FEI Jing-tai:School of Mathematics and Big Data, Chaohu University   

  1. School of Mathematics and Big Data, Chaohu University, Chaohu Anhui 238024
  • Received:2023-03-04 Online:2023-11-25 Published:2024-05-27

摘要: 构建神经网络模型,并证明该模型的稳定性是求解非线性优化的重要问题,矩阵变量神经网络模型是向量型神经网络的拓展,大量研究工作者证明了前者在计算速度与应用方面都更有优势。研究针对一类含有混合约束的非线性规划,提出了一种新的矩阵型投影神经网络,并证明了模型的全局稳定性,模拟实验进一步验证了此结论。

关键词: 矩阵型投影神经网络, 非线性优化, 混合约束, 全局稳定, 计算速度

Abstract: A neural network model is constructed, and the stability of the model is proved to be an important problem in solving nonlinear optimization. Matrix variable neural network model is an extension of vector neural network. A large number of researchers have proved that the former has more advantages in computational speed and application. A new matrix projection neural network is proposed for a class of nonlinear programming with mixed constraints, and the global stability of the model is proved. The simulation experiments further verify the conclusion.

Key words: matrix projection neural network, nonlinear optimization, mixed constraints, global convergence, speed

中图分类号: 

  • TP183