• DocumentCode
    226438
  • Title

    Knowledge-leverage based TSK fuzzy system with improved knowledge transfer

  • Author

    Zhaohong Deng ; Yizhang Jiang ; Longbing Cao ; Shitong Wang

  • Author_Institution
    Sch. of Digital Media, Jiangnan Univ., Wuxi, China
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    178
  • Lastpage
    185
  • Abstract
    In this study, the improved knowledge-leverage based TSK fuzzy system modeling method is proposed in order to overcome the weaknesses of the knowledge-leverage based TSK fuzzy system (TSK-FS) modeling method. In particular, two improved knowledge-leverage strategies have been introduced for the parameter learning of the antecedents and consequents of the TSK-FS constructed in the current scene by transfer learning from the reference scene, respectively. With the improved knowledge-leverage learning abilities, the proposed method has shown the more adaptive modeling effect compared with traditional TSK fuzzy modeling methods and some related methods on the synthetic and real world datasets.
  • Keywords
    fuzzy systems; knowledge based systems; learning (artificial intelligence); TSK-FS; adaptive modeling effect; improved knowledge transfer; knowledge-leverage based TSK fuzzy system modeling method; knowledge-leverage learning abilities; transfer learning; Adaptation models; Data models; Educational institutions; Fuzzy systems; Learning systems; Linear programming; Training; Fuzzy modeling; Fuzzy systems; Improved KL-TSK-FS; Knowledge leverage; Missing data; Transfer learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-2073-0
  • Type

    conf

  • DOI
    10.1109/FUZZ-IEEE.2014.6891544
  • Filename
    6891544