• DocumentCode
    2311004
  • Title

    Research on intellectual signal classifier system

  • Author

    Xu, Mei ; Lei, Ming ; Wang, Lianghou ; Fu, Tianbao ; Hu, Haiyang ; Feng, Diqing

  • Author_Institution
    Inst. of Chem. Defense, Beijing, China
  • fYear
    2009
  • fDate
    10-15 May 2009
  • Firstpage
    271
  • Lastpage
    273
  • Abstract
    In this paper, combined-algorithm classifiers are proposed, which include neural network based on genetic algorithm, constant-coefficient fuzzy neural network, and fuzzy system based on genetic algorithm. `Double helix´ problem is used to compare their performances. These classifiers are applied to distinguish NEMP from LEMP. Due to its excellent classification performance, fuzzy classifier based on genetic algorithm has been chosen to hardware implementation. Based on SOPC (system on programmable chip), with a single large-capacity FPGA as the core controller, an intellectual signal classifier platform is built by the reusable IP technology. The correct classification rate is 100% for NEMP and 92% for LEMP respectively.
  • Keywords
    electromagnetic pulse; field programmable gate arrays; fuzzy neural nets; fuzzy systems; genetic algorithms; programmable circuits; signal classification; system-on-chip; FPGA; combined-algorithm classifiers; constant-coefficient fuzzy neural network; double helix problem; fuzzy classifier; fuzzy system; genetic algorithm; intellectual signal classifier system; lightning electromagnetic pulse; nuclear electromagnetic pulse; reusable IP technology; system on programmable chip; Algorithm design and analysis; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Genetic mutations; Inference algorithms; Joining processes; Neural networks; Real time systems; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Real Time Conference, 2009. RT '09. 16th IEEE-NPSS
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-4454-0
  • Type

    conf

  • DOI
    10.1109/RTC.2009.5322008
  • Filename
    5322008