• DocumentCode
    3224705
  • Title

    Neural net approximations to solutions of systems of fuzzy linear equations

  • Author

    Buckley, J.J. ; Hayashi, Yoichi

  • Author_Institution
    Dept. of Math., Alabama Univ., Birmingham, AL, USA
  • Volume
    4
  • fYear
    1997
  • fDate
    9-12 Jun 1997
  • Firstpage
    2355
  • Abstract
    This paper continues previous research (Buckley and Eslami, 1995, Buckley and Hayashi, 1995, Hayashi and Buckley,1996) into using neural nets to solve fuzzy problems. We show how to train neural nets, with certain sign constraints on their weights, using genetic algorithms, to approximate solutions to systems of fuzzy linear equations. This paper presents a new application of layered, feedforward, neural nets with sign restrictions on their weights
  • Keywords
    feedforward neural nets; fuzzy set theory; genetic algorithms; multilayer perceptrons; fuzzy linear equations; fuzzy problems; genetic algorithms; layered feedforward neural nets; neural net approximations; sign constraints; sign restrictions; Arithmetic; Computer science; Equations; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; Mathematics; Neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks,1997., International Conference on
  • Conference_Location
    Houston, TX
  • Print_ISBN
    0-7803-4122-8
  • Type

    conf

  • DOI
    10.1109/ICNN.1997.614433
  • Filename
    614433
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3224705