Sentence examples similar to crisp linguistic from inspiring English sources

The basic components of the considered FLC are briefly presented below: The fuzzification interface gets the values of input variables (e, Δe), performs a scale mapping to transfer the range of their values into corresponding universes of discourse, and performs the function of fuzzification to convert input (crisp) data into linguistic values.

The human requests may have linguistic or crisp forms and a conventional expert system (ES) is not able to overcome the fuzziness in the problem nature.

The first methodology is based on the AHP which allows the evaluation scores from experts to be linguistic expressions, crisp, or fuzzy numbers, while the second is based on AD principles under fuzziness which evaluates the alternatives under objective or subjective criteria with respect to the functional requirements obtained from experts.

The fuzzyfication component maps a crisp input space into appropriate linguistic labels of fuzzy sets known as membership functions.

The fuzzifier converts a crisp set of input data into input linguistic values using fuzzy linguistic variables, fuzzy linguistic terms, and membership functions.

The distinctions between the F-QFD system and the traditional QFD methodology is that the QFD relevant data are symbolized as linguistic terms rather than crisp numbers, and the linguistic data is processed by algorithms embedded in the system's internal environment (Mehrjerdi 2010).

The assessment rating in fuzzy TOPSIS is conducted by using linguistic variables instead of crisp values.

Fuzzification: in this step, crisp input values are mapped into linguistic variables using membership functions.

Therefore, experts need linguistic expressions rather than crisp numerical values for the evaluation of criteria and alternatives.

A major advantage of the method is that it allows experts and engineers to express their opinions on project risk evaluation in linguistic variables rather than crisp values.

The fuzzy inference process involves four main steps (Ross [1995]): a) Fuzzification: in this step, crisp input values are mapped into linguistic variables using membership functions.