• DocumentCode
    1979015
  • Title

    Fuzzy systems as a fusion framework for describing nonlinear flow in porous media

  • Author

    Nazemi, A.-R. ; Akbarzadeh-T, M.-R. ; Hosseini, S.M.

  • Author_Institution
    Dept. of Civil Eng., Ferdowsi Univ. of Mashhad, Iran
  • fYear
    2003
  • fDate
    24-26 July 2003
  • Firstpage
    389
  • Lastpage
    394
  • Abstract
    By increasing the velocity of flow in coarse grain materials, local turbulences are often imposed to the flow. As a result, the flow regime through rockfill structures deviates from linear Darcy law; and nonlinear or non-Darcy flow equations will be applicable. Even though the structures of these nonlinear equations have some physical justifications, they still need empirical studies to estimate parameters of these equations. Hence there is a great deal of uncertainty as an inherent part of the estimation process. In this paper we investigate fuzzy systems paradigm to combine three of the most commonly validated and utilized empirical solutions in the current literature. In this way, the results of the three empirical equations serve as inputs, and the combination framework serve as fusion algorithm. The results show that when learning injected to fuzzy logic based models, the system provides a powerful solution with a strong ability to track reality. Specifically, this paper concludes that ANFIS provide accurate combination framework with greatest performance among the considered conventional alternatives as well as Mamdani structures.
  • Keywords
    adaptive systems; flow through porous media; fuzzy logic; fuzzy neural nets; fuzzy systems; nonlinear equations; ANFIS; Mamdani structure; adaptive neuro-fuzzy inference system; combination framework; estimation process; flow velocity; fusion algorithm; fusion framework; fuzzy logic based model; fuzzy system; grain material; linear Darcy law; non-Darcy flow equation; nonlinear flow equation; porous media; rockfill structure; Availability; Building materials; Civil engineering; Fuzzy logic; Fuzzy systems; Nonlinear equations; Parameter estimation; Power system modeling; Shape; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society, 2003. NAFIPS 2003. 22nd International Conference of the North American
  • Print_ISBN
    0-7803-7918-7
  • Type

    conf

  • DOI
    10.1109/NAFIPS.2003.1226816
  • Filename
    1226816