fuzzy value

fuzzy value

[¦fəz·ē ′val‚yü]
(mathematics)
A membership function of a fuzzy set that serves as the value assigned to a variable.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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The fuzzyfier block turns the actual input value (crisp) into its corresponding fuzzy value (15) and the inference block is the central block where the rules that form the database are processed and used by the processor to solve a particular problem, thus generating the fuzzy output values.
Fuzzy value of the mean of the experts' opinion is presented in Table 6 to determine the priority of the structural sub-criteria.
The values of ambiguous inputs form curves called membership functions where each value is reflected to a fuzzy value. These actions are controlled by the fuzzy logic controller as illustrated by the figure 2.
Figure 8(a) shows the fuzzy value output for Qir, we do not see significant variation in distribution shapes when compared to the original symmetric quasi-Gaussian distribution of the uncertain parameters [p.sub.1], [p.sup.2], [p.sub.3], [p.sub.4] and [p.sub.5].
Each input variable gets a corresponding fuzzy value:
The aggregation operators (resp., fuzzy aggregation operators) are useful models for combining and summarizing a finite set of numerical values (resp., fuzzy values) into a single numerical value (resp., fuzzy value).
Here, the A will represent the fuzzy value of the variable, and B will represent the degree of truth or reliability measure or probability of A; for example, X is A that is referred to as a possibilitic restriction, that is,
The present fuzzy value of a credit default swap [??] is given as
With the use of FNs, it is possible to analyze the real situation into the fuzzy value. Here is the definition of OWA.
Each fuzzy value combination is mapped to a transmission ability rank according to the rules.
For the ease of understanding Atanassov  called [alpha] = ([[mu].sub.a], [v.sub.[alpha]]) which is an intuitionistic fuzzy value (IFV).
The more the qualitative indexes approach the ideal maximal fuzzy value, the more the magnitude approaches 1; by contrast, the more the qualitative indexes approach the ideal minimal fuzzy value, the more the magnitude approaches 0.

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