New results for neutral-type delayed projection neural network to solve linear variational inequalities

被引:8
|
作者
Zhang, Huaguang [1 ]
Huang, Bonan [1 ]
Gong, Dawei [1 ]
Wang, Zhanshan [1 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
来源
NEURAL COMPUTING & APPLICATIONS | 2013年 / 23卷 / 06期
基金
国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词
Non-monotone linear variational inequality; Projection neural network; Neutral-type delay; Global exponential convergence; TIME-VARYING DELAYS; OPTIMIZATION PROBLEMS; STABILITY ANALYSIS; SYSTEMS; EQUATIONS;
D O I
10.1007/s00521-012-1141-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, a neutral-type delayed projection neural network (NDPNN) was developed for solving variational inequality problems. This paper addresses the global stability and convergence of the NDPNN and presents new results for it to solve linear variational inequality (LVI). Compared with existing convergence results for neural networks to solve LVI, our results do not require the LVI that is monotone so as to guarantee the NDPNN that can solve a class of non-monotone LVI. All the results are expressed in terms of linear matrix inequalities, which can be easily checked. Simulation examples demonstrate the effectiveness of the obtained results.
引用
收藏
页码:1753 / 1761
页数:9
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