Announcement for Downloading full text filePlease respect the Copyright Act.
All digital full text dissertation and theses from this website are authorized the copyright owners. These copyrighted full-text dissertation and theses can be only used for academic, research and non-commercial purposes. Users of this website can search, read, and print for personal usage. In respect of the Copyright Act of the Republic of China, please do not reproduce, distribute, change, or edit the content of these dissertations and theses without any permission. Please do not create any work based upon a pre-existing work by reproduction, Adaptation, Distribution or other means.
URN etd-0910108-132530 Statistics This thesis had been viewed 2333 times. Download 467 times. Author Jiun-Da Jiang Author's Email Address No Public. Department Electrical Engineering Year 2007 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 59 Title THE CONTROL SYSTEM DESIGN OF THE SELF-ORGANIZING ADAPTIVE FUZZY NEURAL NETWORK FOR LINEAR INDUCTION MOTOR Keyword varied learning rates self-organizing adaptive neural network adaptive neural network self-organizing varied learning rates Abstract This thesis focuses on using the self-organization-fuzzy-neural-network (SOFNN) control system to accomplish the periodic motion control of the linear induction motor (LIM) drive. The structure of the fuzzy-neural-network (FNN) is incorporated into the self-organization concept to form the SOFNN control system for alleviating the computation burden. Moreover, the adaptive laws for network parameters are derived in the sense of the Lyapunov stability theorem, and then the stability of the control system can be guaranteed under the occurrence of system uncertainties and external disturbance. The learning-rate parameters of the SOFNN have a significant effect on the network performance. Therefore, the control system employs the varied learning-rate parameters to increase the training effect. The convergence analyses of the output error are based on the discrete-type Lyapunov function to assure the convergence of the output error. Finally, there are three cases with the parameter variations and the time-varying external disturbances are applied in the simulations. The proposed SOFNN control is compared with the adaptive FNN control system. Besides, the effectiveness of the SOFNN control system is verified by the simulations. Advisor Committee Hung-Ching Lu - advisor
none - co-chair
none - co-chair
Files Date of Defense 2008-07-31 Date of Submission 2008-09-10