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
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.