Abstract
The paper presents a novel method of intuitionistic fuzzy defuzzification that was called Optimal Representation Defuzzification or shortly ORD-method. In the method, in the first step membership and non-membership functions of inputs and of the system output are transformed in interval Type 2 membership functions. Then the inference process is realized to determine activation degrees of the output fuzzy sets (rules’ conclusions). Next, for all activated MFs one fuzzy set optimally representing them is determined. And, in the end, one crisp value optimally representing this set is found as defuzzification result. In the ORD method each of rules is treated as a local system expert and activation degree of the rule as coefficient of its competence in the inference process. The approach used in the ORD-method is considerably different from the approach of Mamdani inference. To facilitate the ORD-method understanding in the paper it was explained on the example of intuitionistic fuzzy controller of the fan speed of a room heater.
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Piegat, A., Tomaszewska, K. (2018). Optimal Representation (ORD) Method of Intuitionistic Fuzzy Defuzzification. In: Atanassov, K., et al. Uncertainty and Imprecision in Decision Making and Decision Support: Cross-Fertilization, New Models and Applications. IWIFSGN 2016. Advances in Intelligent Systems and Computing, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-319-65545-1_8
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DOI: https://doi.org/10.1007/978-3-319-65545-1_8
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