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The defense date of the thesis is 2006-08-01
The current date is 2019-03-22
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URN etd-0731106-115928 Statistics This thesis had been viewed 2237 times. Download 12 times. Author Yi-Kai Huang Author's Email Address email@example.com Department Electrical Engineering Year 2005 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 47 Title ON-LINE SPEED CONTROL OF PERMANENT-MAGNET SYNCHRONOUS MOTOR USING SELF-CONSTRUCTING RECCURENT FUZZY NEURAL NETWORK Keyword fuzzy neural network fuzzy neural network Abstract Combining the merits of the self-constructing fuzzy neural network (SCFNN) and the recurrent neural network (RNN), this thesis is proposed to a self-constructing recurrent fuzzy neural network (SCRFNN). Two learning phases are adopted in the proposed network. One is the structure learning phase which is to the partition of input space. The other is the parameter learning which is based on the supervised gradient-decent method using a delta law. The SCRFNN is applied to control the speed of a permanent--magnet synchronous motor to track periodic reference trajectories
In addition, we use fuzzy-neural network to determination of a suitable error term and to train the parameter of the SCRFNN on-line. Finally, the simulation results show that the control effort and chattering of the SCRFNN are smaller than those of SCFNN.
Advisor Committee Hung-Ching Lu - advisor
Ming-Feng Yeh - co-chair
Ta-Hsiung Hung - co-chair
Files Date of Defense 2006-06-14 Date of Submission 2006-08-01