• DocumentCode
    2205532
  • Title

    Research on non-linearity rectification of sensor systems

  • Author

    Chen, Junjie ; Huang, Weiyi

  • Author_Institution
    Dept. of Instrum. Sci. & Eng., Southeast Univ., Nanjing
  • fYear
    2004
  • fDate
    25-25 June 2004
  • Firstpage
    176
  • Lastpage
    180
  • Abstract
    Genetic neural network model of solving the problems on nonlinearity rectification of sensor systems, was put forward, for the shortcoming of least square and other conventional methods. Computer simulations are given to demonstrate that approximation accuracy of the model is far higher than least square method that are extensively applied conventionally and the model possesses stronger robustness through adopting the standpoints and methods in this paper. And the research indicates that the model can be also used to realize nonlinearity rectification in other similar systems
  • Keywords
    backpropagation; control systems; genetic algorithms; least mean squares methods; measurement systems; neural nets; rectification; sensors; BP neural network; genetic algorithm; genetic neural network model; least square methods; nonlinearity rectification; sensor systems; Computer simulation; Control systems; Genetic algorithms; Genetic engineering; Instruments; Least squares approximation; Least squares methods; Neural networks; Robustness; Sensor systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Acquisition, 2004. Proceedings. International Conference on
  • Conference_Location
    Hefei
  • Print_ISBN
    0-7803-8629-9
  • Type

    conf

  • DOI
    10.1109/ICIA.2004.1373345
  • Filename
    1373345