• DocumentCode
    2796098
  • Title

    Hardware implementation of Recurrent S_CMAC_GBF based on FPGA

  • Author

    Chiang, Ching-Tsan ; Lin, Yu-Bim ; Hsieh, Chia-Yen

  • Author_Institution
    Dept. of Electr. Eng., Ching Yun Univ., Jungli
  • Volume
    7
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    3845
  • Lastpage
    3850
  • Abstract
    This study is to design and develop the hardware structure of recurrent S-CMAC_GBF (Ching-Tsan Chiang et al., 2004), and to implement and test the hardware structure by using FPGA chip. S_CMAC_GBF has the same learning convergence characteristic as in CMAC_GBF, but with stronger system accuracy. The learning structure of recurrent enables S_CMAC_GBF with the ability to solve dynamic system or time relevant problem. Although S_CMAC_GBF and recurrent S_CMAC_GBF have outstanding learning performances and applications in static and dynamic systems, both are restricted to the huge computer size and input/output speed, therefore, it is hard to expand their applications. This study reduces the system size to IC grade and increase the processing speed from m sec to mu sec. And there are two temporal relevant examples are employed to demonstrate the performance of the hardware implementation.
  • Keywords
    cerebellar model arithmetic computers; field programmable gate arrays; recurrent neural nets; CMAC; FPGA; cerebellar model articulation controller; field programmable gate arrays; learning convergence; time relevant problem; Cybernetics; Field programmable gate arrays; Hardware; Machine learning; FPGA; Hardware Implementation; Recurrent; S_CMAC_GBF; Temporal Relevant;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4621075
  • Filename
    4621075