By I. Burhan Türksen
Fuzzy set and good judgment concept recommend that every one traditional language linguistic expressions are vague and needs to be assessed as an issue of measure. yet normally club measure is an obscure thought which calls for that style 2 club levels be thought of in such a lot functions regarding human determination making schemas. no matter if the club features are limited to be Type1, their combos generate an period - valued kind 2 club. this is often a part of the final end result that Classical equivalences breakdown in Fuzzy concept. hence all classical formulation has to be reassessed with an top and decrease expression which are generated by means of the breakdown of classical formulation.
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Additional resources for An Ontological and Epistemological Perspective of Fuzzy Set Theory
1. 33 Explication Particularly with set and logic theories that do not discuss their philosophical assumptions directly or explicitly, this structured approach may be helpful in looking for an explication what philosophy seems to be implicit in the texts. Since we have argued all set and logic theories rest upon some philosophical assumptions, it is only consistent for us to argue that if these presuppositions are not dealt with explicitly in our writings, they must still be there implicitly. , to make the philosophical unconscious grounding more conscious.
1973)] in which the concept of a linguistic variable and granulation were introduced. The concepts of fuzzy constraint and fuzzy constraint propagation were introduced in ["Calculus of Fuzzy Restrictions"(1975)], and developed more fully in ["A Theory of Approximate Reasoning"(1979)], etc. In these works, there are schemas that show how one gets started with the notion of granulation and first arrive at information and action granules and then apply divide and conquer principle. Next, one identifies crisp and fuzzy information granules, CIG, FIG.
In these works, there are schemas that show how one gets started with the notion of granulation and first arrive at information and action granules and then apply divide and conquer principle. Next, one identifies crisp and fuzzy information granules, CIG, FIG. " and FIG as "age -^ very young + young + middle-aged + old + very old".