Towards More Specific Estimation of Membership Functions for Data-Driven Fuzzy Inference Systems

Caro Fuchs, Anna Wilbik, Uzay Kaymak

Research output: Contribution to conferencePaperAcademic

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
Pages1-8
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Fuzzy Systems 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

ConferenceIEEE International Conference on Fuzzy Systems 2018
Abbreviated titleFUZZ-IEEE
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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