A Projection Neural Network for Constrained Quadratic Minimax Optimization

被引：82

作者：

Liu, Qingshan ^{[1
,2
]}

Wang, Jun ^{[3
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China

[2] Minist Educ, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China

[3] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2015年 / 26卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Global convergence; Lyapunov stability; projection neural network; quadratic minimax optimization; VARIATIONAL-INEQUALITIES; DISCRETE; STABILITY; SUBJECT; DESIGN; MODEL;

D O I：

10.1109/TNNLS.2015.2425301

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.

引用

页码：2891 / 2900

页数：10

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