• DocumentCode
    3698253
  • Title

    Evolving Complex-Valued Interval Type-2 Fuzzy Inference System

  • Author

    K. Subramanian;S. Suresh

  • Author_Institution
    Air Traffic Management Research Institute, Nanyang Technological University, Singapore, 639798
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Interval Type-2 fuzzy systems have been shown to be extremely capable of handling vagueness as well as uncertainty in data, while complex-valued fuzzy sets have been demonstrated to be capable of solving classification problems efficiently. This paper combines their collective advantage to propose a complex-valued Interval Type-2 Fuzzy Inference System (referred to as CIT2FIS). To derive the fuzzy rules, a Recursive Least Squares based algorithm is proposed. The proposed algorithm evolves (add/ prune) and adapts the rules in an evolving online fashion.
  • Keywords
    "Inference algorithms","Fuzzy logic","Learning systems","Fuzzy sets","Inference mechanisms","Uncertainty","Training"
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/FUZZ-IEEE.2015.7338088
  • Filename
    7338088
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3698253