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
Link To Document :
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