| Summary: | 碩士 === 國立臺北科技大學 === 自動化科技研究所 === 102 === In this thesis, a novel modeling method for Takagi-Sugeno (T-S) fuzzy model is proposed. At first, the sample data points are classified by alternative fuzzy c-means (AFCM) algorithm. Based on Xie-Beni index criterion, the optimal numbers of cluster can be obtained and then the numbers of cluster numbers are set as the rule numbers of fuzzy. In addition, by utilizing fuzzy c-regression model (FCRM) algorithm several linear subsystems can be divided from the unknown system. By examining the fuzzy relationship, ant colony optimization (ACO) algorithm and fuzzy c-regression model (FCRM) algorithm are adopted to find the fuzzy relationship between data points and linear subsystems, and construct the initial value of the fuzzy rule parameters. Moreover, the weight recursive least squares (WLRS) method is utilized to obtain the initial premise variables of each fuzzy rule for each linear subsystem and establish the T-S fuzzy model. Lastly, some examples are given to illustrate that our modeling method can provide the better approximation results than some studies.
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