• DocumentCode
    1625223
  • Title

    Fuzzy CMAC structures

  • Author

    Mohajeri, Kamran ; Zakizadeh, Manijeh ; Moaveni, Bijan ; Teshnehlab, Mohammad

  • Author_Institution
    North power Transm. Maintenance Co.y, Sari, Iran
  • fYear
    2009
  • Firstpage
    2126
  • Lastpage
    2131
  • Abstract
    Cerebellum model articulation controller (CMAC) is known as a feedforward neural network (NN) with fast learning and performance. Many improvements have been introduced to it which fuzzy CMAC (FCMAC) is the most important one. Fuzzy CMAC as a neuro fuzzy system increases precision, reduces memory size and makes CMAC differentiable. In addition FCMAC converts CMAC NN as a black box to a white box that its operation is interpretable using fuzzy rules. Fuzzy CMAC has not a unique structure in literature and there are differences in many aspects as membership function, memory layered structure, deffuzification and the fuzzy system applied. Discussing these, this paper reviews fuzzy CMAC different structures in literature.
  • Keywords
    cerebellar model arithmetic computers; feedforward neural nets; fuzzy neural nets; cerebellum model articulation controller; feedforward neural network; fuzzy CMAC; fuzzy rule; membership function; memory layered structure; neuro fuzzy system; Brain modeling; Computational modeling; Control systems; Feedforward neural networks; Fuzzy systems; Neural networks; Power system modeling; Power transmission; Quantization; Robot control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
  • Conference_Location
    Jeju Island
  • ISSN
    1098-7584
  • Print_ISBN
    978-1-4244-3596-8
  • Electronic_ISBN
    1098-7584
  • Type

    conf

  • DOI
    10.1109/FUZZY.2009.5277185
  • Filename
    5277185