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Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models

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Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models. / Angelov, Plamen; Filev, Dimitar.
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on. IEEE, 2005. p. 1068-1073.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Angelov, P & Filev, D 2005, Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models. in Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on. IEEE, pp. 1068-1073, The 2005 IEEE International Conference on Fuzzy Systems FUZZ-IEEE, Reno, Las vegas, USA, 22/05/05. https://doi.org/10.1109/FUZZY.2005.1452543

APA

Angelov, P., & Filev, D. (2005). Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models. In Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on (pp. 1068-1073). IEEE. https://doi.org/10.1109/FUZZY.2005.1452543

Vancouver

Angelov P, Filev D. Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models. In Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on. IEEE. 2005. p. 1068-1073 doi: 10.1109/FUZZY.2005.1452543

Author

Angelov, Plamen ; Filev, Dimitar. / Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models. Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on. IEEE, 2005. pp. 1068-1073

Bibtex

@inproceedings{2cd89d8559da441e8e9b91397270f3e1,
title = "Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models",
abstract = "This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - a computationally efficient procedure for on-line learning TS type fuzzy models. It combines the concept of the scatter as a measure of data density and summarization ability of the TS rules, the use of Cauchy type antecedent membership functions, an aging indicator characterizing the stationarity of the rules, and a recursive least square algorithm to dynamically learn the structure and parameters of the eTS model. (c) IEEE Press",
author = "Plamen Angelov and Dimitar Filev",
note = "{"}{\textcopyright}2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.{"} {"}This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"}; The 2005 IEEE International Conference on Fuzzy Systems FUZZ-IEEE ; Conference date: 22-05-2005 Through 25-05-2005",
year = "2005",
month = may,
day = "22",
doi = "10.1109/FUZZY.2005.1452543",
language = "English",
isbn = "0-7803-9159-4",
pages = "1068--1073",
booktitle = "Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models

AU - Angelov, Plamen

AU - Filev, Dimitar

N1 - "©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

PY - 2005/5/22

Y1 - 2005/5/22

N2 - This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - a computationally efficient procedure for on-line learning TS type fuzzy models. It combines the concept of the scatter as a measure of data density and summarization ability of the TS rules, the use of Cauchy type antecedent membership functions, an aging indicator characterizing the stationarity of the rules, and a recursive least square algorithm to dynamically learn the structure and parameters of the eTS model. (c) IEEE Press

AB - This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - a computationally efficient procedure for on-line learning TS type fuzzy models. It combines the concept of the scatter as a measure of data density and summarization ability of the TS rules, the use of Cauchy type antecedent membership functions, an aging indicator characterizing the stationarity of the rules, and a recursive least square algorithm to dynamically learn the structure and parameters of the eTS model. (c) IEEE Press

U2 - 10.1109/FUZZY.2005.1452543

DO - 10.1109/FUZZY.2005.1452543

M3 - Conference contribution/Paper

SN - 0-7803-9159-4

SP - 1068

EP - 1073

BT - Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

PB - IEEE

T2 - The 2005 IEEE International Conference on Fuzzy Systems FUZZ-IEEE

Y2 - 22 May 2005 through 25 May 2005

ER -