Abstract
In this article, we present a systemic approach toward a fuzzy logic based formalization of an approximate reasoning methodology in a fuzzy resolution, where we derive a truth value of A from both values of B → A and B by some mechanism. For this purpose, we utilize a t-norm fuzzy logic, in which an implication operator is a root of both graduated conjunction and disjunction operators. Furthermore by using an inverse approximate reasoning, we conclude the truth value of A from both values of B → A and B, applying an altogether different mechanism. A current research is utilizing an approximate reasoning methodology, which is based on a similarity relation for a fuzzification, while similarity measure is utilized in fuzzy inference mechanism. This approach is applied to both generalized modus-ponens/modus-tollens syllogisms and is well-illustrated with artificial examples.
Highlights
This study is a continuation of a research, which is based on a proposed t-norm fuzzy logic, presented in [1]
We present a systemic approach toward a fuzzy logic based formalization of an approximate reasoning methodology in a fuzzy resolution, where we derive a truth value of A from both values of B → A and B by some mechanism
We utilize a t-norm fuzzy logic, in which an implication operator is a root of both graduated conjunction and disjunction operators
Summary
How to cite this paper: Tserkovny, A. (2017) A Fuzzy Logic Based Resolution Principal for Approximate Reasoning. How to cite this paper: Tserkovny, A. (2017) A Fuzzy Logic Based Resolution Principal for Approximate Reasoning. Journal of Software Engineering and Applications, 10, 793-823. Received: August 26, 2017 Accepted: September 25, 2017 Published: September 28, 2017
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