DocumentCode :
1439412
Title :
Primal and dual neural networks for shortest-path routing
Author :
Wang, Jun
Author_Institution :
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
Volume :
28
Issue :
6
fYear :
1998
fDate :
11/1/1998 12:00:00 AM
Firstpage :
864
Lastpage :
869
Abstract :
Presents two recurrent neural networks for solving the shortest path problem. Simplifying the architecture of a recurrent neural network based on the primal problem formulation, the first recurrent neural network called the primal routing network has less complex connectivity than its predecessor. Based on the dual problem formulation, the second recurrent neural network called the dual routing network has even simpler architecture. While being simple in architecture, the primal and dual routing networks are capable of shortest-path routing like their predecessor
Keywords :
directed graphs; minimisation; neural net architecture; recurrent neural nets; dual neural networks; dual routing network; primal neural networks; primal routing network; recurrent neural networks; shortest-path routing; Approximation algorithms; Costs; Neural networks; Path planning; Recurrent neural networks; Robots; Routing; Shortest path problem; Telecommunication traffic; Transportation;
fLanguage :
English
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4427
Type :
jour
DOI :
10.1109/3468.725357
Filename :
725357
Link To Document :
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