Towards more specific estimation of membership functions for data-driven fuzzy inference systems

C.E.M. Fuchs, A.M. Wilbik, U. Kaymak

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
4 Downloads (Pure)

Abstract

Many fuzzy inference systems are built estimating their parameters from data. In particular, Takagi-Sugeno systems have been used a lot in data-driven fuzzy modeling. In this paper, we investigate one step in the data-driven identification of these models, namely the antecedent estimation when fuzzy clustering is used for estimating antecedent memberships and fuzzy rules. We propose removing noise coming from cluster membership values to obtain more specific antecedent sets, which is important for the interpretability of the models. The results obtained and presented in this paper show that this additional step leads to improved performance of the fuzzy model and higher specificity of the antecedent sets.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509060207
DOIs
Publication statusPublished - Jul 2018
Event2018 IEEE International Conference on Fuzzy Systems (FUZZ 2018) - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 IEEE International Conference on Fuzzy Systems (FUZZ 2018)
Abbreviated titleFUZZ 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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