Sentence examples for fuzzy input from inspiring English sources

Non-linear mapping between fuzzy input vectors and crisp output is performed.

In reality, however, the fuzzy input variables of a model are often interactive.

In this study, the mathematical properties of the important measure of the fuzzy input uncertainty are discussed and proved.

The global sensitivity indicator is the important measure of the fuzzy input uncertainty, which extends Borgonovo's measure.

The results of numerical examples and engineering examples show that the proposed importance measure can effectively describe the effect of fuzzy input uncertainty on fuzzy structural response.

Therefore, we have also applied our Fuzzy Calculator to a case study where the interactivity between the fuzzy input variables is identified through possibilistic clustering.

In earlier work, we have developed a general tool, called Fuzzy Calculator, to propagate epistemic uncertainty regarding non-interactive fuzzy input variables.

The results illustrate that this Fuzzy Calculator is an efficient tool to propagate epistemic uncertainty regarding non-interactive as well as interactive fuzzy input variables.

Kao and Chyu [26] introduced the method of LS under fuzzy environment to handle fuzzy observations in regression analysis for three cases: crisp input-fuzzy output, fuzzy input-fuzzy output, and non-triangular fuzzy observations.

Fuzzification transforms the non-fuzzy input variable measurements into the fuzzy set (linguistic) variable that is a clearly defined boundary.

To map the non-fuzzy input values to fuzzy linguistic terms and vice versa, membership functions are used in the fuzzification and defuzzification steps.