A new neural network for solving quadratic programming problems with equality and inequality constraints

作     者：Yang, Yongqing Cao, Jinde Xu, Xianyun Hu, Manfeng Gao, Yun 

作者机构：Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Southeast Univ Dept Math Nanjing 210096 Jiangsu Peoples R China King Abdulaziz Univ Fac Sci Dept Math Jeddah 21589 Saudi Arabia 

出 版 物：《MATHEMATICS AND COMPUTERS IN SIMULATION》 (系统模拟中的数学与计算机)

年 卷 期：2014年第101卷

页      面：103-112页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Key Research Foundation of Science and Technology of the Ministry of Education of China Program for Innovative Research Team of Jiangnan University Academy of Finland (AKA) Funding Source: Academy of Finland (AKA) 

主　　题：Neural network Convergence Stability Quadratic programming Positive semidefinite 

摘      要：A new neural network is proposed in this paper for solving quadratic programming problems with equality and inequality constraints. Comparing with the existing neural networks for solving such problems, the proposed neural network has fewer neurons and an one-layer architecture. The proposed neural network is proven to be global convergence. Furthermore, illustrative examples are given to show the effectiveness of the proposed neural network. (C) 2014 IMACS. Published by Elsevier B.V. All rights reserved.