Home > Research > Publications & Outputs > Identification of Evolving Rule-based Models.

Research

Associated organisational unit

Electronic data

Links

Text available via DOI:

Keywords

View graph of relations

Identification of Evolving Rule-based Models.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Identification of Evolving Rule-based Models. / Angelov, Plamen; Buswell, Richard.
In: IEEE Transactions on Fuzzy Systems, Vol. 10, No. 5, 10.2002, p. 667-677.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Angelov, P & Buswell, R 2002, 'Identification of Evolving Rule-based Models.', IEEE Transactions on Fuzzy Systems, vol. 10, no. 5, pp. 667-677. https://doi.org/10.1109/TFUZZ.2002.803499

APA

Angelov, P., & Buswell, R. (2002). Identification of Evolving Rule-based Models. IEEE Transactions on Fuzzy Systems, 10(5), 667-677. https://doi.org/10.1109/TFUZZ.2002.803499

Vancouver

Angelov P, Buswell R. Identification of Evolving Rule-based Models. IEEE Transactions on Fuzzy Systems. 2002 Oct;10(5):667-677. doi: 10.1109/TFUZZ.2002.803499

Author

Angelov, Plamen ; Buswell, Richard. / Identification of Evolving Rule-based Models. In: IEEE Transactions on Fuzzy Systems. 2002 ; Vol. 10, No. 5. pp. 667-677.

Bibtex

@article{a86bbb0bde134d3d985c1a0c7b28aac3,
title = "Identification of Evolving Rule-based Models.",
abstract = "An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy Systems",
keywords = "adaptive nonlinear control air-conditioning component modeling behavior modeling complex processes evolving fuzzy rule-based models fault detection fault diagnostics forecasting fuzzy rules identification incremental unsupervised learning informative potential knowledge extraction noniterative update performance analysis ranking robotics rule-base structure DCS-publications-id, art-465, DCS-publications-personnel-id, 82",
author = "Plamen Angelov and Richard Buswell",
note = "{"}{\textcopyright}2002 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.{"}",
year = "2002",
month = oct,
doi = "10.1109/TFUZZ.2002.803499",
language = "English",
volume = "10",
pages = "667--677",
journal = "IEEE Transactions on Fuzzy Systems",
issn = "1063-6706",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "5",

}

RIS

TY - JOUR

T1 - Identification of Evolving Rule-based Models.

AU - Angelov, Plamen

AU - Buswell, Richard

N1 - "©2002 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 - 2002/10

Y1 - 2002/10

N2 - An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy Systems

AB - An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy Systems

KW - adaptive nonlinear control air-conditioning component modeling behavior modeling complex processes evolving fuzzy rule-based models fault detection fault diagnostics forecasting fuzzy rules identification incremental unsupervised learning informative pote

KW - art-465

KW - DCS-publications-personnel-id

KW - 82

U2 - 10.1109/TFUZZ.2002.803499

DO - 10.1109/TFUZZ.2002.803499

M3 - Journal article

VL - 10

SP - 667

EP - 677

JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

SN - 1063-6706

IS - 5

ER -