Lilly Schoen: Created page with "{{GlossaryTermTemp |Abbreviation=FL |Ambiguities=Logical analysis, vagueness |SubtermOf=extension of multivalued logic, t-norms |Definition=1. fuzzy sets (Mengen) model concep..."

2017-11-03T09:42:05Z

Created page with "{{GlossaryTermTemp |Abbreviation=FL |Ambiguities=Logical analysis, vagueness |SubtermOf=extension of multivalued logic, t-norms |Definition=1. fuzzy sets (Mengen) model concep..."

New page

{{GlossaryTermTemp
|Abbreviation=FL
|Ambiguities=Logical analysis, vagueness
|SubtermOf=extension of multivalued logic, t-norms
|Definition=1. fuzzy sets (Mengen) model concepts and objects in the real world. advantage of the fuzzy logic is, to categorize data in the set (Menge) (oposite: binary logic with crisp boundary or values). A fuzzy set is a collection of related items which belong to that set to different degrees. Information from fuzzy sets can combinded using rules to make decisions.
fuzzy ruels take partially true facts, finds out to what degree they are true.
2. The standard set of truth degrees for fuzzy logics is the real unit interval [0,1] with its natural ordering ≤ , ranging from total falsity (represented by 0) to total truth (represented by 1) through a continuum of intermediate truth degrees.
|Sources=http://de.mathworks.com/help/fuzzy/what-is-fuzzy-logic.html?s_tid=gn_loc_drop; https://www.youtube.com/watch?v=P8wY6mi1vV8; https://plato.stanford.edu/entries/logic-fuzzy/
}}
Author: Anas Naddour

Fuzzy logic - Revision history

Lilly Schoen: Created page with "{{GlossaryTermTemp |Abbreviation=FL |Ambiguities=Logical analysis, vagueness |SubtermOf=extension of multivalued logic, t-norms |Definition=1. fuzzy sets (Mengen) model concep..."