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A fuzzy controller with evolving structure.

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A fuzzy controller with evolving structure. / Angelov, Plamen.
In: Information Sciences, Vol. 161, No. 1-2, 05.04.2004, p. 21-35.

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

Harvard

Angelov, P 2004, 'A fuzzy controller with evolving structure.', Information Sciences, vol. 161, no. 1-2, pp. 21-35. https://doi.org/10.1016/j.ins.2003.03.006

APA

Angelov, P. (2004). A fuzzy controller with evolving structure. Information Sciences, 161(1-2), 21-35. https://doi.org/10.1016/j.ins.2003.03.006

Vancouver

Angelov P. A fuzzy controller with evolving structure. Information Sciences. 2004 Apr 5;161(1-2):21-35. doi: 10.1016/j.ins.2003.03.006

Author

Angelov, Plamen. / A fuzzy controller with evolving structure. In: Information Sciences. 2004 ; Vol. 161, No. 1-2. pp. 21-35.

Bibtex

@article{b90f6fb044f7422697d0ec8880cd588d,
title = "A fuzzy controller with evolving structure.",
abstract = "An approach to on-line design of fuzzy controllers of Takagi-Sugeno type with gradually evolving structure is treated in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi-Sugeno systems. The output of the plant under control (including its dynamic) and the respective control signal has been memorised and stored in on-line mode. These data has been used to train in a non-iterative, recursive way the fuzzy controller. The indirect adaptive control approach has been used in combination with the novel on-line identification technique. This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode with recursive, non-iterative learning. The method is illustrated with an example from air-conditioning systems, though it has wider potential applications.",
keywords = "evolving fuzzy rule-based systems, indirect adaptive control, on-line clustering, on-line identification, recursive least squares estimation, DCS-publications-id, art-586, 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, Information Sciences 161 (1-2), 2004, {\textcopyright} ELSEVIER.",
year = "2004",
month = apr,
day = "5",
doi = "10.1016/j.ins.2003.03.006",
language = "English",
volume = "161",
pages = "21--35",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",
number = "1-2",

}

RIS

TY - JOUR

T1 - A fuzzy controller with evolving structure.

AU - Angelov, Plamen

N1 - The final, definitive version of this article has been published in the Journal, Information Sciences 161 (1-2), 2004, © ELSEVIER.

PY - 2004/4/5

Y1 - 2004/4/5

N2 - An approach to on-line design of fuzzy controllers of Takagi-Sugeno type with gradually evolving structure is treated in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi-Sugeno systems. The output of the plant under control (including its dynamic) and the respective control signal has been memorised and stored in on-line mode. These data has been used to train in a non-iterative, recursive way the fuzzy controller. The indirect adaptive control approach has been used in combination with the novel on-line identification technique. This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode with recursive, non-iterative learning. The method is illustrated with an example from air-conditioning systems, though it has wider potential applications.

AB - An approach to on-line design of fuzzy controllers of Takagi-Sugeno type with gradually evolving structure is treated in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi-Sugeno systems. The output of the plant under control (including its dynamic) and the respective control signal has been memorised and stored in on-line mode. These data has been used to train in a non-iterative, recursive way the fuzzy controller. The indirect adaptive control approach has been used in combination with the novel on-line identification technique. This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode with recursive, non-iterative learning. The method is illustrated with an example from air-conditioning systems, though it has wider potential applications.

KW - evolving fuzzy rule-based systems

KW - indirect adaptive control

KW - on-line clustering

KW - on-line identification

KW - recursive least squares estimation

KW - DCS-publications-id

KW - art-586

KW - DCS-publications-credits

KW - dsp-fa

KW - DCS-publications-personnel-id

KW - 82

U2 - 10.1016/j.ins.2003.03.006

DO - 10.1016/j.ins.2003.03.006

M3 - Journal article

VL - 161

SP - 21

EP - 35

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

IS - 1-2

ER -