Enhanced prediction accuracy of fuzzy models using multiscale estimation Conference Paper

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.

name of conference

• 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

authors

• Nounou, Hazem
• Nounou, Mohamed

published proceedings

• 2004 43RD IEEE CONFERENCE ON DECISION AND CONTROL (CDC), VOLS 1-5

author list (cited authors)

• Nounou, M. N., & Nounou, H. N.

citation count

• 1

complete list of authors

• Nounou, MN||Nounou, HN

publication date

• January 2004

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... IEEE Conference on Decision & Control / IEEE Control Systems Society. IEEE Conference on Decision & Control  Journal

keywords

• 40 Engineering
• 46 Information And Computing Sciences
• 4602 Artificial Intelligence
• Bioengineering

Digital Object Identifier (DOI)

• 10.1109/CDC.2004.1429628

International Standard Book Number (ISBN) 10

• 0-7803-8682-5

start page

• 5170

end page

• 5175

volume

• 5

URL

• http://dx.doi.org/10.1109/cdc.2004.1429628