A Discrete-time Recurrent Neural Network with Global Exponential Stability for Constrained Linear Variational Inequalities

被引：0
|
作者：
Liu Qingshan [1 ]
Yang Wankou [1 ]
机构：
[1] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, 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：
Discrete-time recurrent neural network; Globally exponentially stable; Linear variational inequalities; QUADRATIC-PROGRAMMING PROBLEMS; OPTIMIZATION PROBLEMS; SUBJECT; DESIGN;
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：
In this paper, a discrete-time recurrent neural network with global exponential stability is proposed for solving constrained linear variational inequalities. Compared with the existing neural networks for linear variational inequalities, the proposed neural network in this paper has lower model complexity with only one-layer structure. The global exponential stability of the neural network can be guaranteed under some mild conditions. Simulation results show the performance and characteristics of the proposed neural network.
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页码：3296 / 3301
页数：6
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