• 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