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Function approximation based on fuzzy rules extracted from partitioned numerical data.

R Thawonmas1, S Abe

  • 1Dept. of Inf. Syst. Eng., Kochi Univ. of Technol.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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This study introduces an efficient fuzzy rule extraction method for function approximation using numerical data. The approach converts problems into pattern classification, enabling accurate fuzzy rule generation and outperforming neural networks in tests.

Area of Science:

  • Computational Intelligence
  • Machine Learning
  • Data Mining

Background:

  • Function approximation is crucial in various scientific and engineering domains.
  • Existing methods may lack efficiency or direct rule interpretability from numerical data.

Purpose of the Study:

  • To develop an efficient method for extracting fuzzy rules directly from numerical input-output data.
  • To apply the method to function approximation problems and compare its performance.

Main Methods:

  • Converting function approximation to pattern classification by defining output variable intervals as classes.
  • Partitioning input space data for each class to approximate class regions using hyperboxes or ellipsoidal regions.
  • Extracting fuzzy rules from approximated regions and defuzzifying membership degrees for final approximation.

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Main Results:

  • The method successfully extracts fuzzy rules from numerical data for function approximation.
  • Demonstrated effectiveness on both synthetic nonlinear and real-world water purification plant problems.
  • Achieved comparable or superior performance against a neural network-based method.

Conclusions:

  • The proposed method offers an efficient way to generate interpretable fuzzy rules for function approximation.
  • It provides a viable alternative to traditional methods like neural networks, especially when interpretability is desired.