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Title page for etd-0828107-160131


URN etd-0828107-160131 Statistics This thesis had been viewed 1650 times. Download 711 times.
Author Wen-Yen Hsu
Author's Email Address No Public.
Department Electrical Engineering
Year 2006 Semester 2
Degree Master Type of Document Master's Thesis
Language English Page Count 66
Title RADIAL BASIS FUNCTION NETWORK BASED AUTOMATIC GENERATION FUZZY NEURAL  NETWORK CONTROLLER FOR PERMANENT MAGNET LINEAR SYNCHRONOUS
MOTOR
Keyword
  • permanent magnet linear synchronous motor
  • fuzzy neural network
  • radial basis function network
  • radial basis function network
  • fuzzy neural network
  • permanent magnet linear synchronous motor
  • Abstract In this thesis, a radial basis function network (RBFN) based automatic generation fuzzy neural network (AGFNN) is proposed to control the rotor position of the permanent magnet linear synchronous motor (PMLSM) to track the period reference trajectories. The proposed RBFN based AGFNN not only has the advantages of the back-propagation algorithm, in which the parameter of the connected weights are adjusted but also has the advantages of the switching law, momentum term and RBFN, in which the tracking error and steady state responses will be betterment. The structure learning is based on the Mahalanobis distance and the parameter learning is based on the back-propagation algorithm. The simulation results of the proposed RBFN-based AGFNN with the periodic reference trajectories show that the tracking error and steady state responses have better performance, own the robustness performance under the parameter variation and external load disturbance.
    Advisor Committee
  • Hung-Ching Lu - advisor
  • Files indicate in-campus access at 3 years and off-campus access at 3 years
    Date of Defense 2007-07-31 Date of Submission 2007-08-28


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