Solving the Assignment Problem Using Continuous-Time and Discrete-Time Improved Dual Networks

被引：20

作者：

Hu, Xiaolin ^{[1
,2
]}

Wang, Jun ^{[3
]}

机构：

[1] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China

[2] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China

[3] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2012年 / 23卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Analog circuits; assignment problem; linear programming; quadratic programming; sorting problem; RECURRENT NEURAL-NETWORKS; STABILITY; DESIGN;

D O I：

10.1109/TNNLS.2012.2187798

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The assignment problem is an archetypal combinatorial optimization problem. In this brief, we present a continuous-time version and a discrete-time version of the improved dual neural network (IDNN) for solving the assignment problem. Compared with most assignment networks in the literature, the two versions of IDNNs are advantageous in circuit implementation due to their simple structures. Both of them are theoretically guaranteed to be globally convergent to a solution of the assignment problem if only the solution is unique.

引用

页码：821 / 827

页数：7

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