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Fuzzyfication of the used factors is made by the membership functions (1), (2) and (3).
The fuzzy sets N, E, and P are characterized by the membership functions shown in Figure 6.
In the framework of the IT2 T S fuzzy model, the parameter uncertainty is handled by the membership functions with lower and upper bounds.
The mean component is obtained by linearly combining μ l parameters (i.e., ) and the weights of the linear combination are determined by the membership functions Ĩ l (c).
The linguistic values of,,, and also representing fuzzy concepts "Negative," "Equal," and "Positive," respectively, are characterized by the membership functions as shown in Figure 7, where and.
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First we choose the parameter set by using the membership functions.
By reviewing the membership functions, it is determined that Trapmf membership function has better performance than other membership functions especially in the monthly model.
The parameter uncertainties in the nonlinear plant can be captured effectively by utilizing the membership functions with upper and lower bounds.
The low-level interpretability of fuzzy models refers to fuzzy model interpretability achieved by optimizing the membership functions in terms of semantic criteria on fuzzy set level, while the high-level interpretability refers to fuzzy model interpretability obtained by dealing with the coverage, completeness, and consistency of the rules in terms of the criteria on fuzzy rule level.
This step is done by using the membership functions.
Implementation was accomplished by defining the membership functions and iterating to obtain the degree of membership for each pixel.
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