Fuzzy Logic, Fuzzy Conditional Inference and Fuzzy Reasoning based on Belief and Disbelief

EasyChair Preprint 3552, version 1

Abstract

Zadeh defined fuzzy Sets       for Uncertain Information with single Fuzzy membershipfunction A = µ_A(x ), where A is Fuzzy Set and x Є X. In this paper, the Fuzzy set is defined by A= { µ_A^Belief(x), µ _A^Disbelief(x)} with the two Fuzzy membership functionsbased on Belief and Disbelief. The Fuzzy Set with two Fuzzy membership functions will give more evidence to fuzzy information. Fuzzy Logic and Fuzzy inference are sproposed based on the two fuzzy membership functions. In this paper, the fuzzy conditional inference for “ if … then …” and “if … then … else” is also proposed with two fuzzy membership functions. Fuzzy Certainty Factor is defined with the difference of Belief Fuzzy Membership Function and Disbelief Fuzzy Membership Function to elinate the conflict of evidence in Uncertain Information

Keyphrases: Fuzzy Logic, Fuzzy membership functions, fuzzy inference, fuzzy modulations

@booklet{EasyChair:3552,
  author    = {Venkata Subba Reddy Poli},
  title     = {Fuzzy Logic, Fuzzy Conditional Inference and	Fuzzy  Reasoning based on  Belief and Disbelief},
  howpublished = {EasyChair Preprint 3552},
  year      = {EasyChair, 2020}}