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2017 | OriginalPaper | Chapter

A Method for the Detection of the Most Suitable Fuzzy Implication for Data Applications

Authors : Panagiotis Pagouropoulos, Christos D. Tzimopoulos, Basil K. Papadopoulos

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

Fuzzy implications are widely used in applications where propositional logic is applicable. In cases where a variety of fuzzy implications can be used for a specific application, it is important that the optimal candidate be chosen in order valuable inference be drawn from a given set of data. This study introduces a method for detecting the most suitable fuzzy implication among others under consideration, which incorporates an algorithm forthe separation of two extreme cases. According to the truth values of the corresponding fuzzy propositions the optimal implication is one of these two extremes. An example involving five such relations is used to illustrate the procedure of the method. The results obtained verify that the resulting implication is the optimal operator for inference making from the data.

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Title: A Method for the Detection of the Most Suitable Fuzzy Implication for Data Applications
Authors: Panagiotis Pagouropoulos
Christos D. Tzimopoulos
Basil K. Papadopoulos
Publisher: Springer International Publishing
Book: Engineering Applications of Neural Networks
Print ISBN: 978-3-319-65171-2

Electronic ISBN: 978-3-319-65172-9

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-65172-9_21

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