Sentence examples for by the membership function from inspiring English sources
Every measurement of the variables is standardized by the membership function of quality evaluation on the interval [0,1], referring to the trophic status clarified in the Carlson Trophic State Index.
The number of rules is denoted by M and A i is the (multivariate) antecedent fuzzy set of the ith rule defined by the membership function mu_{i} left( x right):Re^{n} to left[ {0,;;1} right] (8).
To understand the meaning of these fuzzy rules, the following situation is assumed: if the fuzzy directional values obtained by the membership function for the SMALL fuzzy set are close to one, then there is neither motion nor low-noise presence in the central pixel component.
The imprecise parameters can be expressed as fuzzy numbers and the variability is characterized by the membership function (MF) which may be triangular or trapezoidal as the most common MF types used in reliability application are triangular or trapezoidal functions (Yadav et al. 2003).
If (tilde{A}) is a fuzzy number with different left height and right height as defined by the membership function (1) and the inverse membership function given by (2) then the left integral value of (tilde{A}) is defined as begin{aligned} I_{rm L} tilde{A})=int _{0}^{h_{rm L}}mu _{1}^{-1} y){mathrm{d}}y end{aligned} (6).
If (tilde{A}) is a fuzzy number with different left height and right height as defined by the membership function (1) and the inverse membership function given by (2) then the right integral value of (tilde{A}) is defined as begin{aligned} I_{rm R} tilde{A})=int _{0}^{h_{rm R}}mu _{3}^{-1} y){mathrm{d}}y end{aligned} (7).
b) Fuzzy methods In fuzzy analysis methods, the uncertain parameters are described by using the membership function, and fuzzy membership can be obtained by subjective investigation [46, 47].
ANFIS can be used to train the FIS model by modifying the membership function parameters based on error chosen criterion to cope with the training data.
By using the membership function given in Figures 1 and 2 and the prediction method of fuzzy logic in Equation 1 with the weighting factor α (=0.7), the output curve of the traffic phases can be plotted as shown in Figures 5 and 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.
