DocumentCode
1460792
Title
Primal and dual assignment networks
Author
Wang, Jun
Author_Institution
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
Volume
8
Issue
3
fYear
1997
fDate
5/1/1997 12:00:00 AM
Firstpage
784
Lastpage
790
Abstract
This paper presents two recurrent neural networks for solving the assignment problem. Simplifying the architecture of a recurrent neural network based on the primal assignment problem, the first recurrent neural network, called the primal assignment network, has less complex connectivity than its predecessor. The second recurrent neural network, called the dual assignment network, based on the dual assignment problem, is even simpler in architecture than the primal assignment network. The primal and dual assignment networks are guaranteed to make optimal assignment. The applications of the primal and dual assignment networks for sorting and shortest-path routing are discussed. The performance and operating characteristics of the dual assignment network are demonstrated by means of illustrative examples
Keywords
duality (mathematics); neural net architecture; operations research; optimisation; recurrent neural nets; complex connectivity; dual assignment networks; optimal assignment; primal assignment network; recurrent neural network architecture; shortest-path routing; sorting; Cost function; Design optimization; Helium; Job production systems; Neural networks; Pattern classification; Recurrent neural networks; Routing; Scheduling; Sorting;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.572114
Filename
572114
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