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 language | English |
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Title of host publication | 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Number of pages | 8 |
ISBN (Electronic) | 9781509060207 |
DOIs | |
Publication status | Published - Jul 2018 |
Event | 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 |
Conference
Conference | 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 |
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Abbreviated title | FUZZ 2018 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 8/07/18 → 13/07/18 |