• DocumentCode
    3572748
  • Title

    A new neural networks-based model of hysteresis

  • Author

    Lianwei Ma ; Yu Shen ; Jinrong Li ; Xinlong Zhao

  • Author_Institution
    Sch. of Autom. & Electr. Eng., Zhejiang Univ. of Sci. & Technol., Hangzhou, China
  • fYear
    2014
  • Firstpage
    1540
  • Lastpage
    1543
  • Abstract
    A new approach to constructing hysteretic operator is proposed in this paper. Based on the hysteretic operator, the input space of neural networks is expanded from 1-dimension to 2-dimension and the multi-value mapping of hysteresis is transformed into one-to-one mapping. Based on the expanded input space, a neural network is employed to approximate hysteresis. The result of an experimental example suggests the proposed approach is effective.
  • Keywords
    approximation theory; hysteresis; neural nets; expanded space method; hysteretic operator; multivalue mapping; neural network; Artificial neural networks; Biological neural networks; Data models; Hysteresis; Neurons; Predictive models; expanded space method; hysteresis; hysteretic operator; neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2014 11th World Congress on
  • Type

    conf

  • DOI
    10.1109/WCICA.2014.7052948
  • Filename
    7052948
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3572748