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- W2113116368 abstract "Fuzzy system modeling (FSM) is one of the most prominent system modeling tools in analyzing the data in the presence of uncertainty. Linguistic fuzzy rulebase (LFR) structure, in which both the antecedent and consequent variables are represented by fuzzy sets, is the most well known fuzzy rulebase structure in the literature. The proposed FSM method identifies LFR system model by executing fuzzy C-Means (FCM) clustering method. One of the sources of uncertainty in system modeling is the uncertainty in selecting learning parameters. In order to capture this uncertainty in a more realistic way, the antecedent and consequent variables are represented by using type 2 fuzzy sets that are constructed by executing FCM method with different level of fuzziness, m, values. The proposed system modeling approach is applied on a well-known benchmark data set where the goal is to predict the price of a stock. After comparing the results with the ones obtained with other system modeling tools, it can be claimed successful results are achieved." @default.
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- W2113116368 date "2005-03-31" @default.
- W2113116368 modified "2023-09-26" @default.
- W2113116368 title "A new fuzzy inference approach based on mamdani inference using discrete type 2 fuzzy sets" @default.
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- W2113116368 doi "https://doi.org/10.1109/icsmc.2004.1400667" @default.
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