A Novel Modeling Method for T-S Fuzzy Model

• Main Authors: Hong-En Su, 蘇宏恩
• Other Authors: 蔡舜宏
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/dt6shy

碩士 === 國立臺北科技大學 === 自動化科技研究所 === 101 === A novel modeling method for T-S fuzzy model is proposed in this thesis. Firstly, fuzzy c-means algorithm is adopted to classify the data points and determined the numbers of the cluster. In addition, by defining the cluster numbers as the rule number, several linear subsystems can be divided from unknown system. Moreover, particle swarm optimization (PSO) algorithm and fuzzy c-regression model (FCRM) algorithm are adopted to find the fuzzy relationship between the data points and these linear subsystems, and construct the initial value of the fuzzy rule parameters. Finally, the weight recursive least squares method is adopted to obtain the optimal values of the system parameters and establish the T-S fuzzy model. Some models are illustrated to demonstrate that our modeling method can provide the more precious model than some well-known methods.