Enhanced prediction accuracy of fuzzy models using multiscale estimation

Mohamed N. Nounou; Hazem N. Nounou

doi:10.1109/cdc.2004.1429628

Enhanced prediction accuracy of fuzzy models using multiscale estimation

Mohamed N. Nounou^*
, Hazem N. Nounou

^*Corresponding author for this work

United Arab Emirates University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The presence of measurement noise in the data used in empirical modeling can have a drastic effect on the accuracy of estimated models, and thus need to be removed for improved models accuracy. Multiscale representation of data has shown great noise-removal ability when used in data filtering. In this paper, this ability is exploited to improve the prediction accuracy of the Takagi-Sugeno (TS) fuzzy model by developing a multiscale fuzzy (MSF) system identification algorithm. The algorithm relies on constructing multiple fuzzy models at multiple scales using the scaled signal approximations of the input-output data, and then selecting the optimum multiscale model which maximizes the prediction signal-to-noise ratio. The developed algorithm is shown to outperform its time domain counterpart through a simulated example.

Original language	English
Title of host publication	2004 43rd IEEE Conference on Decision and Control (CDC)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5170-5175
Number of pages	6
ISBN (Print)	0780386825
DOIs	https://doi.org/10.1109/cdc.2004.1429628
Publication status	Published - 2004
Externally published	Yes
Event	43rd IEEE Conference on Decision and Control, CDC 2004 - Nassau, Bahamas Duration: 14 Dec 2004 → 17 Dec 2004

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	5
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	43rd IEEE Conference on Decision and Control, CDC 2004
Country/Territory	Bahamas
City	Nassau
Period	14/12/04 → 17/12/04

Access to Document

10.1109/cdc.2004.1429628

Cite this

Nounou, M. N., & Nounou, H. N. (2004). Enhanced prediction accuracy of fuzzy models using multiscale estimation. In 2004 43rd IEEE Conference on Decision and Control (CDC) (pp. 5170-5175). Article FrC04.2 (Proceedings of the IEEE Conference on Decision and Control; Vol. 5). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/cdc.2004.1429628

@inproceedings{32ef4af3925240d8849798dee178c5c8,

title = "Enhanced prediction accuracy of fuzzy models using multiscale estimation",

abstract = "The presence of measurement noise in the data used in empirical modeling can have a drastic effect on the accuracy of estimated models, and thus need to be removed for improved models accuracy. Multiscale representation of data has shown great noise-removal ability when used in data filtering. In this paper, this ability is exploited to improve the prediction accuracy of the Takagi-Sugeno (TS) fuzzy model by developing a multiscale fuzzy (MSF) system identification algorithm. The algorithm relies on constructing multiple fuzzy models at multiple scales using the scaled signal approximations of the input-output data, and then selecting the optimum multiscale model which maximizes the prediction signal-to-noise ratio. The developed algorithm is shown to outperform its time domain counterpart through a simulated example.",

author = "Nounou, \{Mohamed N.\} and Nounou, \{Hazem N.\}",

year = "2004",

doi = "10.1109/cdc.2004.1429628",

language = "English",

isbn = "0780386825",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5170--5175",

booktitle = "2004 43rd IEEE Conference on Decision and Control (CDC)",

address = "United States",

note = "43rd IEEE Conference on Decision and Control, CDC 2004 ; Conference date: 14-12-2004 Through 17-12-2004",

}

Nounou, MN & Nounou, HN 2004, Enhanced prediction accuracy of fuzzy models using multiscale estimation. in 2004 43rd IEEE Conference on Decision and Control (CDC)., FrC04.2, Proceedings of the IEEE Conference on Decision and Control, vol. 5, Institute of Electrical and Electronics Engineers Inc., pp. 5170-5175, 43rd IEEE Conference on Decision and Control, CDC 2004, Nassau, Bahamas, 14/12/04. https://doi.org/10.1109/cdc.2004.1429628

Enhanced prediction accuracy of fuzzy models using multiscale estimation. / Nounou, Mohamed N.; Nounou, Hazem N.
2004 43rd IEEE Conference on Decision and Control (CDC). Institute of Electrical and Electronics Engineers Inc., 2004. p. 5170-5175 FrC04.2 (Proceedings of the IEEE Conference on Decision and Control; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Nounou, Hazem N.

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N2 - The presence of measurement noise in the data used in empirical modeling can have a drastic effect on the accuracy of estimated models, and thus need to be removed for improved models accuracy. Multiscale representation of data has shown great noise-removal ability when used in data filtering. In this paper, this ability is exploited to improve the prediction accuracy of the Takagi-Sugeno (TS) fuzzy model by developing a multiscale fuzzy (MSF) system identification algorithm. The algorithm relies on constructing multiple fuzzy models at multiple scales using the scaled signal approximations of the input-output data, and then selecting the optimum multiscale model which maximizes the prediction signal-to-noise ratio. The developed algorithm is shown to outperform its time domain counterpart through a simulated example.

AB - The presence of measurement noise in the data used in empirical modeling can have a drastic effect on the accuracy of estimated models, and thus need to be removed for improved models accuracy. Multiscale representation of data has shown great noise-removal ability when used in data filtering. In this paper, this ability is exploited to improve the prediction accuracy of the Takagi-Sugeno (TS) fuzzy model by developing a multiscale fuzzy (MSF) system identification algorithm. The algorithm relies on constructing multiple fuzzy models at multiple scales using the scaled signal approximations of the input-output data, and then selecting the optimum multiscale model which maximizes the prediction signal-to-noise ratio. The developed algorithm is shown to outperform its time domain counterpart through a simulated example.

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DO - 10.1109/cdc.2004.1429628

M3 - Conference contribution

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SN - 0780386825

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 5170

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BT - 2004 43rd IEEE Conference on Decision and Control (CDC)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 43rd IEEE Conference on Decision and Control, CDC 2004

Y2 - 14 December 2004 through 17 December 2004

ER -

Enhanced prediction accuracy of fuzzy models using multiscale estimation

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