Abstract
This paper presents fuzzy models which rules were extracted from numerical data using clonal selection, subtractive, fuzzy C-means, Gustafson–Kessel clustering algorithms, implemented in the MATLAB code. These algorithms were used for the identification of parameters in the fuzzy model Sugeno-type. There are two testing examples: Trip data and DWP data set from the multi-detector sensor. Fuzzy model of the fire risk index was built based on the laboratory data measurements. The results are shown in tables and graphs.
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Mrozek, B. (2014). Immune Algorithm for Fuzzy Models Generation. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Recent Advances in Automation, Robotics and Measuring Techniques. Advances in Intelligent Systems and Computing, vol 267. Springer, Cham. https://doi.org/10.1007/978-3-319-05353-0_19
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DOI: https://doi.org/10.1007/978-3-319-05353-0_19
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