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Title page for etd-0712104-194000


URN etd-0712104-194000 Statistics This thesis had been viewed 2559 times. Download 992 times.
Author Kuan-Zung Chen
Author's Email Address No Public.
Department Electrical Engineering
Year 2003 Semester 2
Degree Master Type of Document Master's Thesis
Language English Page Count 42
Title Using credit assignment and GBF with dynamic learning rate to enhance the ability of low-dimensional CMAC
Keyword
  • CMAC
  • CMAC
  • Abstract Conventional CMAC has a memory size problem at high dimensional input space. Using the neural network structure composed of small CMACs can efficiently solve this problem. By using the advantage of credit assignment and Gaussian basis function, we use it to improve the performance of the neural network structure composed of small CMACs. The simulation result shows that it still has weakness.
    The dynamic learning rate and repeat training are the concepts from conventional CMAC. We use the dynamic learning rate and repeat training to overcome the weakness. The simulation shows that dynamic learning rate indeed have better performance, and dynamic learning rate with Gaussian basis function could achieve the best result.
    Advisor Committee
  • Ta-Hsiung Hung - advisor
  • Hung-Ching Lu - co-chair
  • Ming-Feng Yeh - co-chair
  • Files indicate access worldwide
    Date of Defense 2004-06-28 Date of Submission 2004-07-12


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