• DocumentCode
    3706899
  • Title

    MIMO evolving learning based on maximum likelihood algorithm applied to black box fuzzy modeling for systems identification design

  • Author

    Orlando Donato Rocha Filho;Ginalber Luiz de Oliveira Serra

  • Author_Institution
    Department of Electroelectronics, Laboratory of Computational Intelligence Applied to Techonology, Federal Institute of Education, Science and Technology, Sã
  • Volume
    1
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    199
  • Lastpage
    206
  • Abstract
    This paper presents an overview of a specific application to 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","Prototypes","Maximum likelihood estimation","Partitioning algorithms","Algorithm design and analysis"
  • Publisher
    ieee
  • Conference_Titel
    Informatics in Control, Automation and Robotics (ICINCO), 2015 12th International Conference on
  • Type

    conf

  • Filename
    7350467
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3706899