Analog circuits for solving a class of variational inequality problems

被引：12

作者：

Zhao, You ^{[1
]}

He, Xing ^{[1
]}

Huang, Tingwen ^{[2
]}

Han, Qi ^{[3
]}

机构：

[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing Key Lab Nonlinear Circuits & Intelligen, Chongqing 400715, Peoples R China

[2] Texas A&M Univ Qatar, Doha 5825, Qatar

[3] Chongqing Univ Sci & Technol, Coll Elect & Informat Engn, Chongqing 401331, Peoples R China

来源：

NEUROCOMPUTING | 2018年 / 295卷

基金：

中国博士后科学基金;

关键词：

Variational inequality problems (VIPs); Projection operator; Analog circuits; PROJECTION NEURAL-NETWORK; OPTIMIZATION PROBLEMS;

D O I：

10.1016/j.neucom.2018.03.016

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present the analog circuits to solve a class of variational inequality problems (VIPs) based on the projection neural network (PNN) and inertial projection neural network (IPNN) algorithms. The proposed circuits are normative and only require basic circuit elements. The optimal solutions of VIPs are equivalent to the stable output voltages of the associated circuits. This paper also shows how to design analog circuits with projection operators (box constrains set and sphere constrains set) on the basis of PNN and IPNN algorithms. As a result, a class of variational inequality problems can be solved by proposed circuit frameworks. The effectiveness and superiority (with less computing time) of the proposed analog circuits are expound by simulating on three examples. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：142 / 152

页数：11

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