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Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS

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Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS. / Angelov, P.; Lughofer, E.; Klement, E. P.

Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American. IEEE, 2005. p. 31-35.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Angelov, P, Lughofer, E & Klement, EP 2005, Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS. in Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American. IEEE, pp. 31-35, 2005 Annual Meeting of the North American Fuzzy Information Processing Society Annual Conference, Ann Arbor, Michigan, USA, 21/06/05. https://doi.org/10.1109/NAFIPS.2005.1548502

APA

Angelov, P., Lughofer, E., & Klement, E. P. (2005). Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS. In Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American (pp. 31-35). IEEE. https://doi.org/10.1109/NAFIPS.2005.1548502

Vancouver

Angelov P, Lughofer E, Klement EP. Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS. In Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American. IEEE. 2005. p. 31-35 https://doi.org/10.1109/NAFIPS.2005.1548502

Author

Angelov, P. ; Lughofer, E. ; Klement, E. P. / Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS. Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American. IEEE, 2005. pp. 31-35

Bibtex

@inproceedings{a9624bbe8d3f4e7c8ae548829009ccb6,
title = "Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS",
abstract = "In this paper two approaches for the incremental data-driven learning of one of the most effective fuzzy model, namely of so-called Takagi-Sugeno type, are compared. The algorithms that realise these approaches include not only adaptation of linear parameters in fuzzy systems appearing in the rule consequents, but also incremental learning and evolution of premise parameters appearing in the membership functions (i.e. fuzzy sets) in sample mode together with a rule learning strategy. In this sense the proposed methods are applicable for fast model training tasks in various industrial processes, whenever there is a demand of online system identification in order to apply models representing nonlinear system behaviors to system monitoring, online fault detection or open-loop control. An evaluation of the incremental learning algorithms are included at the end of the paper, where a comparison between conventional batch modelling methods for fuzzy systems and the incremental learning methods demonstrated in this paper is made with respect to model qualities and computation time. This evaluation is based on high dimensional data coming from an industrial measuring process as well as from a known source on the Internet, which underlines the usage of the new method for fast online identification tasks.",
keywords = "Incremental learning , adaptation of parameters , evolving Takagi-Sugeno fuzzy systems , online identification , rule learning",
author = "P. Angelov and E. Lughofer and Klement, {E. P.}",
year = "2005",
month = jun,
day = "22",
doi = "10.1109/NAFIPS.2005.1548502",
language = "English",
isbn = "0-7803-9187-X",
pages = "31--35",
booktitle = "Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American",
publisher = "IEEE",
note = "2005 Annual Meeting of the North American Fuzzy Information Processing Society Annual Conference ; Conference date: 21-06-2005 Through 25-06-2005",

}

RIS

TY - GEN

T1 - Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS

AU - Angelov, P.

AU - Lughofer, E.

AU - Klement, E. P.

PY - 2005/6/22

Y1 - 2005/6/22

N2 - In this paper two approaches for the incremental data-driven learning of one of the most effective fuzzy model, namely of so-called Takagi-Sugeno type, are compared. The algorithms that realise these approaches include not only adaptation of linear parameters in fuzzy systems appearing in the rule consequents, but also incremental learning and evolution of premise parameters appearing in the membership functions (i.e. fuzzy sets) in sample mode together with a rule learning strategy. In this sense the proposed methods are applicable for fast model training tasks in various industrial processes, whenever there is a demand of online system identification in order to apply models representing nonlinear system behaviors to system monitoring, online fault detection or open-loop control. An evaluation of the incremental learning algorithms are included at the end of the paper, where a comparison between conventional batch modelling methods for fuzzy systems and the incremental learning methods demonstrated in this paper is made with respect to model qualities and computation time. This evaluation is based on high dimensional data coming from an industrial measuring process as well as from a known source on the Internet, which underlines the usage of the new method for fast online identification tasks.

AB - In this paper two approaches for the incremental data-driven learning of one of the most effective fuzzy model, namely of so-called Takagi-Sugeno type, are compared. The algorithms that realise these approaches include not only adaptation of linear parameters in fuzzy systems appearing in the rule consequents, but also incremental learning and evolution of premise parameters appearing in the membership functions (i.e. fuzzy sets) in sample mode together with a rule learning strategy. In this sense the proposed methods are applicable for fast model training tasks in various industrial processes, whenever there is a demand of online system identification in order to apply models representing nonlinear system behaviors to system monitoring, online fault detection or open-loop control. An evaluation of the incremental learning algorithms are included at the end of the paper, where a comparison between conventional batch modelling methods for fuzzy systems and the incremental learning methods demonstrated in this paper is made with respect to model qualities and computation time. This evaluation is based on high dimensional data coming from an industrial measuring process as well as from a known source on the Internet, which underlines the usage of the new method for fast online identification tasks.

KW - Incremental learning

KW - adaptation of parameters

KW - evolving Takagi-Sugeno fuzzy systems

KW - online identification

KW - rule learning

U2 - 10.1109/NAFIPS.2005.1548502

DO - 10.1109/NAFIPS.2005.1548502

M3 - Conference contribution/Paper

SN - 0-7803-9187-X

SP - 31

EP - 35

BT - Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American

PB - IEEE

T2 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society Annual Conference

Y2 - 21 June 2005 through 25 June 2005

ER -