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"As a result, we remain naïve about the long, essential testing period when our actions transform (or fail to transform) fuzzy, undefined possibilities into concrete choices we can evaluate".
The centre of gravity method is adopted to transform fuzzy sets into crisp values (i.e. defuzzification algorithm).
The membership functions to transform fuzzy output datasets into crisp outputs are constant values (for Takagi-Sugeno approach).
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Defuzzification is a process of transforming fuzzy output inferred from the fuzzy inference system (FIS) to a crisp value.
Fuzzification is the process of transforming the real value inputs into fuzzy values whereas the defuzzification process is the way of transforming fuzzy outputs into real values.
It applies fuzzy reasoning mechanisms to obtain outputs and carries out the computation using fuzzy rules; defuzzification: done by a defuzzification module which transforms fuzzy outputs back to crisp values.
The concept of alpha-level set is introduced to transform the fuzzy membership functions to fuzzy intervals so that ambiguous coefficients can be defuzzified.
We also propose weighted functions of fuzzy numbers as a defuzzification procedure in order to transform estimated fuzzy claim costs into a crisp real equivalent.
The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree and a Petri nets technique to transform the fuzzy rule into the FRVPNs model.
In this paper, we present a novel algorithm to estimate direction and length of motion blur, using Radon transform and fuzzy set concepts.
The idea is based on Zadeh's extension principle to transform the fuzzy geometric programming problem into a pair of two-level of mathematical programs.
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