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From Type 1 to Full Type N Fuzzy System Models
I. Burhan Turksen

We first brief review the essential Type 1 Fuzzy System models. Next we state the well- known FCM algorithm which lets one to extract Type 1 membership values from a given data set for the development of Type 1 fuzzy system models as a foundation for the development of Full Type 2 fuzzy system models. For this purpose, we describe how one can generate Full Type 2 membership value distributions for a development of second order fuzzy system models with our proposed second order data analysis. We also suggest that a recursive restatement of FCM algorithm can allow the generation of Full Type 3 and … Type n fuzzy system models if one were to investigate such system models in the future. We present our results graphicallyfor TD_Stockprice data with respect to three validity indeces: 1) Xie-Beni’s, 2) Çelikyılmaz-Türkşen’s and 3) Bezdek’s.

Keywords: Zadeh’s rulebase model, Takagi and Sugeno’s model, Türksen’s fuzzy regression model, FCM algorithm, Type 1 fuzzy system models, Full Type 2 fuzzy system models, TD_Stockprice data, Çelikyılmaz-Türksen, Bezdek indeces

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