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2016•Unpublished venueRequires access

Fuzzy Certainty Factor for incomplete information

Venkata Subba Reddy Poli

Open publisher page 1 citations

Abstract

Fuzzy logic has flexibility in defining fuzzy sets. Zadeh defined fuzzy sets for incomplete information with single fuzzy membership. The two fold fuzzy set will give more information than the single membership function. In this paper, two fuzzy set is studied with two membership functions “Belief” and “Disbelief”. The Fuzzy Certainty Factor (FCF) is difference between “Belief” and “disbelief”. The Fuzzy Certainty Factor is studied differently. The fuzzy logic and fuzzy reasoning are studied for FCF. The medical diagnosis is given as an example.

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What this paper is about

Fuzzy logic has flexibility in defining fuzzy sets. Zadeh defined fuzzy sets for incomplete information with single fuzzy membership. The two fold fuzzy set will give more information than the single membership function. In this paper, two fuzzy set is studied with two membership functions “Belief” and “Disbelief”. The Fuzzy Certainty Factor (FCF) is difference between “Belief” and “disbelief”. The Fuzzy Certainty Factor is studied differently. The fuzzy logic and fuzzy reasoning are studied for FCF. The medical diagnosis is given as an example.

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Available abstract

Fuzzy logic has flexibility in defining fuzzy sets. Zadeh defined fuzzy sets for incomplete information with single fuzzy membership. The two fold fuzzy set will give more information than the single membership function. In this paper, two fuzzy set is studied with two membership functions “Belief” and “Disbelief”. The Fuzzy Certainty Factor (FCF) is difference between “Belief” and “disbelief”. The Fuzzy Certainty Factor is studied differently. The fuzzy logic and fuzzy reasoning are studied for FCF. The medical diagnosis is given as an example.

Key concepts: Fuzzy classification, Type-2 fuzzy sets and systems, Fuzzy set operations, Defuzzification, Fuzzy logic, Membership function, Fuzzy number, Fuzzy set

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