Date Added: Apr 2011
Precision assumes that the parameters of a model represent exactly either the authors' perception of the phenomenon modeled or the features of the real system that has been modeled. Generally precision indicates that the model is unequivocal, that is, it contains no ambiguities. By crisp they mean yes-or-no type rather than more-or-less type. In conventional dual logic, for instance, a statement can be true or false- and definitely nothing in between. Vagueness, imprecision and uncertainty have so far been modeled by classical set-theoretic approach. According to this approach, borderline elements can be either put into the set or should be kept outside it.