A high performance neural network for solving quadratic programming with hybrid constraints

被引：0
|
作者：
Xu, Xianyun [1 ]
Yang, Yongqing [1 ]
Gao, Yun [1 ]
机构：
[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Peoples R China
来源：
PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年
基金：
中国国家自然科学基金;
关键词：
Neural network; Convergence; Stability; Quadratic programming; Positive semidefinite; OPTIMIZATION PROBLEMS; ACTIVATION FUNCTION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A high performance neural network is proposed in this paper for solving quadratic programming problems with hybrid 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.
引用
收藏
页码：3261 / 3266
页数：6
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