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Coupland, S. and John, R. (2008) Type-2 Fuzzy Logic, Modeling Uncertainty. In: Bustince, H., Herrera, F. and Montero, J., Eds., Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, Vol. 220, Springer, Berlin, Heidelberg, 3-22.
https://doi.org/10.1007/978-3-540-73723-0_1

has been cited by the following article:

• TITLE: A t-Norm Fuzzy Logic for Approximate Reasoning

  AUTHORS: Alex Tserkovny

  KEYWORDS: Fuzzy Logic, t-Norm, Implication, Antecedent, Consequent, Modus-Ponens, Fuzzy Conditional Inference Rule

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.10 No.7, June 29, 2017

  ABSTRACT: A t-norm fuzzy logic is presented, in which a triangular norm (t-norm) plays the role of a graduated conjunction operator. Based on this fuzzy logic we develop methods for fuzzy reasoning in which antecedents and consequents involve fuzzy conditional propositions of the form “If x is A then y is B”, with A and B being fuzzy concepts (fuzzy sets). In this study, we present a systemic approach toward fuzzy logic formalization for approximate reasoning. We examine statistical characteristics of the proposed fuzzy logic. As the matter of practical interest, we construct a set of fuzzy conditional inference rules on the basis of the proposed fuzzy logic. Important features of these rules are investigated.