A Discrete-time Switching Neural Network for Quadratic Programming

被引：0
|
作者：
Chen, S. [1 ]
Li, S. [2 ]
Liang, Y. [1 ]
Lou, Y. [3 ]
机构：
[1] Shenzhen Inst Informat Technol, Key Lab Visual Media Proc & Transmiss, Shenzhen 518029, Guangdong, Peoples R China
[2] Stevens Inst Technol, Hoboken, NJ 07030 USA
[3] Yiwu Ind & Commercial Coll, Sch Mechatron & Informat, Yiwu, Zhejiang, Peoples R China
来源：
2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2012年
基金：
中国国家自然科学基金;
关键词：
Neural network; quadratic programming; contraction theory; global convergence; OPTIMIZATION; SUBJECT; ROBOT;
D O I：
暂无
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 discrete-time neural network with a switching structure to solve a general quadratic programming problem in real time. Compared with existing ones for solving quadratic programming problems, the proposed neural network model has a simple architecture and uses a limited number of neurons to solve the problem, irrespective of the dimension of the decision variables or the number of constraints. The global convergence of the model is proven using contraction theory. Simulations are performed to demonstrate the effectiveness of the proposed method.
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页数：5
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