• DocumentCode
    2855579
  • Title

    The Implementation of FEM and RBF Neural Network in EIT

  • Author

    Wang, Peng ; Li, Hong-li ; Li-li Xie ; Sun, Yi-cai

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Hebei Univ. of Technol., Tianjin, China
  • fYear
    2009
  • fDate
    1-3 Nov. 2009
  • Firstpage
    66
  • Lastpage
    69
  • Abstract
    With the rapid development of electronic technology, semiconductor section resistivity measurement is receiving increasing attention. This paper applies electrical impedance tomography (EIT) technology to semiconductor resistivity measurements. FEM is applied to solve the EIT forward problem. Mathematical description of partial differential equation, equivalent variation differential problem, element characteristic matrix and the assembly rule of general matrix are given for calculation. To solve the EIT inverse problem, a new method of image reconstruction algorithm based on RBF neural network is proposed. This method can well adapt to non-linear and ill-posed characteristics of EIT. The simulation experiment results indicate that the RBF algorithm can improve the reconstruction image´s quality and the accuracy obviously.
  • Keywords
    electric impedance imaging; electronic engineering computing; finite element analysis; image reconstruction; partial differential equations; radial basis function networks; RBF neural network; electrical impedance tomography technology; electronic technology; element characteristic matrix; equivalent variation differential problem; finite element method; image quality reconstruction; partial differential equation; semiconductor section resistivity measurement; Conductivity measurement; Equations; Finite element methods; Image reconstruction; Impedance; Intelligent networks; Inverse problems; Neural networks; Tomography; Voltage; Electrical impedance tomography; Finite element method; RBF neural network; semiconductor section resistivity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Networks and Intelligent Systems, 2009. ICINIS '09. Second International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4244-5557-7
  • Electronic_ISBN
    978-0-7695-3852-5
  • Type

    conf

  • DOI
    10.1109/ICINIS.2009.26
  • Filename
    5365675