Soft Sensor Based on Fuzzy Model Identification
نویسندگان
چکیده
This paper presents an approach to build a soft sensor based on computational intelligence techniques. The goal is to identify fuzzy models from numerical data. First of all, the fuzzy model input variables are selected from the secondary variables set by applying Kohonen maps. Then, the Lipschitz quotients are used to select the lag structure of the fuzzy model. A fuzzy clustering algorithm is applied to find an initial rule base, and, to conclude the identification process, this initial rule base is simplified by merging the similar membership functions. The validity of the proposed identification method is demonstrated by the development of a soft sensor to infer the top composition of a distillation column. Copyrigth c ©
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