Fuzzy Logic Modeling Algorithm Based on Cluster Estimation Method

• Main Authors: Jiunn-Yeong Yang, 楊俊勇
• Other Authors: Jin-Chern Chiou
• Format: Others
• Language: en_US
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/11220308935283501095

博士 === 國立交通大學 === 電機與控制工程系 === 87 === A fuzzy logic modeling algorithm have been proposed for semiconductor fabrication processes. The fuzzy logic modeling algorithm consists of a cluster estimation method and backpropagation learning method to construct a number of modeling structures from the training data. A decision rule based on the multiple correlation coefficient is used to obtain the optimum structure of fuzzy modeling from using the testing data. Upon the optimum structure has been reached, the gradient-descent method is used to refer the parameters of the final fuzzy model using both training and testing data. The algorithm has been applied to various nonlinear functions and a chemical vapor deposition process. The results demonstrate the efficiency and effectiveness of the proposed fuzzy logic model in comparison with existing fuzzy logic models and artificial neural network models.