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