• 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