An inertial projection neural network for solving inverse variational inequalities

被引：10
|
作者：
Ju, Xingxing [1 ]
Li, Chuandong [1 ]
He, Xing [1 ]
Feng, Gang [2 ]
机构：
[1] Southwest Univ, Sch Elect & Informat Engn, Natl & Local Joint Engn Lab Intelligent Transmiss, Chongqing 400715, Peoples R China
[2] City Univ Hong Kong, Dept Biomed Engn, Hong Kong, Peoples R China
来源：
NEUROCOMPUTING | 2020年 / 406卷
基金：
中国国家自然科学基金;
关键词：
GLOBAL EXPONENTIAL STABILITY; OPTIMIZATION PROBLEMS; DESIGN;
D O I：
10.1016/j.neucom.2020.04.023
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel inertial projection neural network (IPNN) is proposed for solving inverse variational inequalities (IVIs) in this paper. It is shown that the IPNN has a unique solution under the condition of Lipschitz continuity and that the solution trajectories of the IPNN converge to the equilibrium solution asymptotically if the corresponding operator is co-coercive. Finally, several examples are presented to illustrtae the effectiveness of the proposed IPNN. © 2020 Elsevier B.V.
引用
收藏
页码：99 / 105
页数：7
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