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URN etd-0910108-134015 Statistics This thesis had been viewed 2265 times. Download 419 times. Author Ming-Feng Chiang 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 92 Title STUDY ON ADAPTIVE SELF-CONSTRUCTING FUZZY NEURAL NETWORK USING ASYMMETRIC GAUSSIANMEMBERSHIP FUNCTIONS Keyword sliding-mode control system parameter estimation robust control self-constructing asymmetric fuzzy neural networ self-constructing asymmetric fuzzy neural networ robust control system parameter estimation sliding-mode control Abstract The self-constructing fuzzy neural network using asymmetric Gaussian membership function (SCAFNN) controller is proposed in this thesis. The asymmetric Gaussian membership function is utilized to upgrade learning capability and flexibility of SCAFNN. The proposed adaptive SCAFNN sliding-mode control system comprises a computation controller and a robust controller. The adaptive SCAFNN system is utilized as the principal controller, in which an SCAFNN estimator is designed to estimate the parameter of system dynamics on-line. The robust controller is not only utilized to attenuate the effects of approximation error between the real nonlinear system and an approximate SCAFNN dynamics but also developed to estimate the uncertainty bound. Mahalanobis distance (M-distance) method in this thesis is employed as the criterion to identify the neurons will be generated / eliminated or not. The on-line adaptive laws are derived based on the sense of Lyapunov so that stability of the system can be guaranteed. Finally, the simulation results of the examples are provided to demonstrate the performance and effectiveness of the proposed controller. Advisor Committee Hung-Ching Lu - advisor
none - co-chair
none - co-chair
Files Date of Defense 2008-07-31 Date of Submission 2008-09-11