• DocumentCode
    3662046
  • Title

    Instrumental variable based maximum likelihood evolving fuzzy algorithm for nonlinear system identification

  • Author

    Orlando Donato Rocha Filho;Ginalber Luiz de Oliveira Serra

  • Author_Institution
    Federal Institute of Education, Science and Technology, Sã
  • fYear
    2015
  • fDate
    6/1/2015 12:00:00 AM
  • Firstpage
    83
  • Lastpage
    88
  • Abstract
    This paper presents an overview of a specific application fo computational intelligence techniques, specifically, evolving fuzzy systems: online fuzzy inference system with Takagi-Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion online with instrumental variable recursive parameter estimation. The performance and application of the proposed methodology is based on the black box modeling.
  • Keywords
    "Clustering algorithms","Instruments","Covariance matrices","Maximum likelihood estimation","Prototypes","Partitioning algorithms"
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics (ISIE), 2015 IEEE 24th International Symposium on
  • Electronic_ISBN
    2163-5145
  • Type

    conf

  • DOI
    10.1109/ISIE.2015.7281448
  • Filename
    7281448
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3662046