Enhanced prediction accuracy of fuzzy models using multiscale estimation

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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 languageEnglish
Pages (from-to)5170-5175
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume5
Publication statusPublished - 2004
Externally publishedYes

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Fuzzy Model
Prediction
Fuzzy Identification
Representation of data
Noise Removal
Multiscale Model
Takagi-Sugeno Fuzzy Model
Multiple Scales
Multiple Models
System Identification
Fuzzy Systems
Time Domain
Filtering
Maximise
Fuzzy systems
Output
Approximation
Signal to noise ratio
Identification (control systems)
Modeling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety

Cite this

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