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URN etd-0823107-185810 Statistics This thesis had been viewed 2954 times. Download 1353 times. Author Jui-Yiao Su Author's Email Address No Public. Department Electrical Engineering Year 2006 Semester 2 Degree Ph.D. Type of Document Doctoral Dissertation Language English Page Count 117 Title Fuzzy Model Identification by FCRM Clustering with Novel Cluster Validity Criteria and its Applications Keyword fuzzy model based control fuzzy model identification fuzzy c-regression model (FCRM) clustering cluster validity criteria cluster validity criteria fuzzy c-regression model (FCRM) clustering fuzzy model identification fuzzy model based control Abstract In this thesis, an effective approach is developed to establish fuzzy models for a given nonlinear system. Firstly, the fuzzy c-regression model (FCRM) clustering technique is applied to partition the product space of the given input-output data into hyper-plan-shaped clusters. Each cluster is essentially a basis of the fuzzy rule that describes the system behavior, and the number of clusters is just the number of fuzzy rules. Particularly, several novel cluster validity criteria for FCRM clustering are set up to choose the appropriate number of clusters (rules). Once the number of clusters is determined, the consequent parameters of each IF-THEN rule are directly obtained from the functional cluster representatives (linear or affine linear regression models). The antecedent fuzzy sets of each IF-THEN fuzzy rule are acquired by projecting the fuzzy partitions matrix U onto the axes of individual antecedent variable to obtain point-wise defined fuzzy sets and to approximate these point-wise defined fuzzy sets by normal bell-shaped membership functions. Additionally, a check and repartition algorithm is suggested to prevent the inappropriate premise structure where separate regions of data shared the same regression model. Finally, the gradient descent algorithm is included to adjust the fuzzy model precisely. A fuzzy model with compact IF-THEN rules could thus be generated systematically.
Once a reasonably accurate fuzzy model of the consider process is available, it can be used as a part of the fuzzy model based control (FMBC) scheme. In this thesis, two FMBC are proposed for discrete-time nonlinear systems as well. The first FMBC is designed to make the plant track the reference trajectory signal with stable error dynamic. The second FMBC can stabilize the NARMA (nonlinear auto-regressive moving average) system with tracking performance by solving the linear matrix inequalities (LMIs).
Several simulation examples are provided to demonstrate the accuracy and effectiveness of the fuzzy modeling algorithm and the controllers.
Advisor Committee Chung-Chun Kung - advisor
Bor-Sen Chen - co-chair
Chiang-Cheng Chiang - co-chair
Chih-Min Lin - co-chair
none - co-chair
Tzuu-Hseng S. Li - co-chair
Files Date of Defense 2007-07-19 Date of Submission 2007-08-27