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

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

doi:10.1109/FUZZ-IEEE.2018.8491524

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

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

Information Systems IE&IS

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

9 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 language	English
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	https://doi.org/10.1109/FUZZ-IEEE.2018.8491524
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)
Abbreviated title	FUZZ 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

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10.1109/FUZZ-IEEE.2018.8491524

Cite this

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title = "Towards more specific estimation of membership functions for data-driven fuzzy inference systems",

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.",

author = "C.E.M. Fuchs and A.M. Wilbik and U. Kaymak",

year = "2018",

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language = "English",

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note = "2018 IEEE International Conference on Fuzzy Systems (FUZZ 2018), FUZZ 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

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Fuchs, CEM, Wilbik, AM & Kaymak, U 2018, Towards more specific estimation of membership functions for data-driven fuzzy inference systems. in 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) ., 8491524, Institute of Electrical and Electronics Engineers, Piscataway, 2018 IEEE International Conference on Fuzzy Systems (FUZZ 2018), Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/FUZZ-IEEE.2018.8491524

Towards more specific estimation of membership functions for data-driven fuzzy inference systems. / Fuchs, C.E.M.; Wilbik, A.M.; Kaymak, U.

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) . Piscataway : Institute of Electrical and Electronics Engineers, 2018. 8491524.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Wilbik, A.M.

AU - Kaymak, U.

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N2 - 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.

AB - 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.

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M3 - Conference contribution

BT - 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 2018 IEEE International Conference on Fuzzy Systems (FUZZ 2018)

Y2 - 8 July 2018 through 13 July 2018

ER -

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

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