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An Approach for Fuzzy Rule-base Adaptation using On-line Clustering.

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An Approach for Fuzzy Rule-base Adaptation using On-line Clustering. / Angelov, Plamen.
In: International Journal of Approximate Reasoning, Vol. 35, No. 3, 03.2004, p. 275-289.

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

Harvard

Angelov, P 2004, 'An Approach for Fuzzy Rule-base Adaptation using On-line Clustering.', International Journal of Approximate Reasoning, vol. 35, no. 3, pp. 275-289. https://doi.org/10.1016/j.ijar.2003.08.006

APA

Angelov, P. (2004). An Approach for Fuzzy Rule-base Adaptation using On-line Clustering. International Journal of Approximate Reasoning, 35(3), 275-289. https://doi.org/10.1016/j.ijar.2003.08.006

Vancouver

Angelov P. An Approach for Fuzzy Rule-base Adaptation using On-line Clustering. International Journal of Approximate Reasoning. 2004 Mar;35(3):275-289. doi: 10.1016/j.ijar.2003.08.006

Author

Angelov, Plamen. / An Approach for Fuzzy Rule-base Adaptation using On-line Clustering. In: International Journal of Approximate Reasoning. 2004 ; Vol. 35, No. 3. pp. 275-289.

Bibtex

@article{6ed0033a762949c2a05470a07ad85efd,
title = "An Approach for Fuzzy Rule-base Adaptation using On-line Clustering.",
abstract = "A recursive approach for adaptation of fuzzy rule-based model structure has been developed and tested. It uses on-line clustering of the input–output data with a recursively calculated spatial proximity measure. Centres of these clusters are then used as prototypes of the centres of the fuzzy rules (as their focal points). The recursive nature of the algorithm makes possible to design an evolving fuzzy rule-base in on-line mode, which adapts to the variations of the data pattern. The proposed algorithm is instrumental for on-line identification of Takagi–Sugeno models, exploiting their dual nature and combined with the recursive modified weighted least squares estimation of the parameters of the consequent part of the model. The resulting evolving fuzzy rule-based models have high degree of transparency, compact form, and computational efficiency. This makes them strongly competitive candidates for on-line modelling, estimation and control in comparison with the neural networks, polynomial and regression models. The approach has been tested with data from a fermentation process of lactose oxidation. (c) 2003 Elsevier Inc. All rights reserved.",
keywords = "On-line clustering, Fuzzy rule-based models identification, Parameter estimation, Takagi–Sugeno fuzzy models, DCS-publications-id, art-572, DCS-publications-credits, dsp-fa, DCS-publications-personnel-id, 82",
author = "Plamen Angelov",
note = "The final, definitive version of this article has been published in the Journal, International Journal of Approximate Reasoning 35 (3), 2004, {\textcopyright} ELSEVIER.",
year = "2004",
month = mar,
doi = "10.1016/j.ijar.2003.08.006",
language = "English",
volume = "35",
pages = "275--289",
journal = "International Journal of Approximate Reasoning",
issn = "0888-613X",
publisher = "Elsevier Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - An Approach for Fuzzy Rule-base Adaptation using On-line Clustering.

AU - Angelov, Plamen

N1 - The final, definitive version of this article has been published in the Journal, International Journal of Approximate Reasoning 35 (3), 2004, © ELSEVIER.

PY - 2004/3

Y1 - 2004/3

N2 - A recursive approach for adaptation of fuzzy rule-based model structure has been developed and tested. It uses on-line clustering of the input–output data with a recursively calculated spatial proximity measure. Centres of these clusters are then used as prototypes of the centres of the fuzzy rules (as their focal points). The recursive nature of the algorithm makes possible to design an evolving fuzzy rule-base in on-line mode, which adapts to the variations of the data pattern. The proposed algorithm is instrumental for on-line identification of Takagi–Sugeno models, exploiting their dual nature and combined with the recursive modified weighted least squares estimation of the parameters of the consequent part of the model. The resulting evolving fuzzy rule-based models have high degree of transparency, compact form, and computational efficiency. This makes them strongly competitive candidates for on-line modelling, estimation and control in comparison with the neural networks, polynomial and regression models. The approach has been tested with data from a fermentation process of lactose oxidation. (c) 2003 Elsevier Inc. All rights reserved.

AB - A recursive approach for adaptation of fuzzy rule-based model structure has been developed and tested. It uses on-line clustering of the input–output data with a recursively calculated spatial proximity measure. Centres of these clusters are then used as prototypes of the centres of the fuzzy rules (as their focal points). The recursive nature of the algorithm makes possible to design an evolving fuzzy rule-base in on-line mode, which adapts to the variations of the data pattern. The proposed algorithm is instrumental for on-line identification of Takagi–Sugeno models, exploiting their dual nature and combined with the recursive modified weighted least squares estimation of the parameters of the consequent part of the model. The resulting evolving fuzzy rule-based models have high degree of transparency, compact form, and computational efficiency. This makes them strongly competitive candidates for on-line modelling, estimation and control in comparison with the neural networks, polynomial and regression models. The approach has been tested with data from a fermentation process of lactose oxidation. (c) 2003 Elsevier Inc. All rights reserved.

KW - On-line clustering

KW - Fuzzy rule-based models identification

KW - Parameter estimation

KW - Takagi–Sugeno fuzzy models

KW - DCS-publications-id

KW - art-572

KW - DCS-publications-credits

KW - dsp-fa

KW - DCS-publications-personnel-id

KW - 82

U2 - 10.1016/j.ijar.2003.08.006

DO - 10.1016/j.ijar.2003.08.006

M3 - Journal article

VL - 35

SP - 275

EP - 289

JO - International Journal of Approximate Reasoning

JF - International Journal of Approximate Reasoning

SN - 0888-613X

IS - 3

ER -