Data Noise Reduction in Neuro-fuzzy Systems

  • Krzysztof Simiński
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)


Some datasets require highly complicated fuzzy models for the best knowledge ability. The complicated models mean poor intelligibility for humans and more calculations for machines. Thus often the model are artificially simplified to save the interpretability. The simplified model (with less rules) have higher error for knowledge generalisation. In order to improve knowledge generalisation in simplified models it is convenient to reduce the complexity of data by reduction of noise in the datasets. The paper presents the algorithm for noise removal based on modified discrete convolution is crisp domain. The experiments reveal that the algorithm can improve the generalisation ability for simplified model of highly complicated data.


Fuzzy Inference System Noise Removal Isosceles Triangle Data Noise Discrete Convolution 
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  1. 1.
    Roberto, M., Almeida, A.: Sistema híbrido neuro-fuzzy-genético para mineração automática de dados. Master’s thesis, Pontifíca Universidade Católica do Rio de Janeiro (2004)Google Scholar
  2. 2.
    Anastasio, M.A., Pan, X., Kao, C.-M.: A general technique for smoothing multi-dimensional datasets utilizing orthogonal expansions and lower dimensional smoothers. In: Proceedings of International Conference on Image Processing, ICIP 1998, October 1998, vol. 2, pp. 718–721 (1998)Google Scholar
  3. 3.
    Czogała, E., Łȩski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Series in Fuzziness and Soft Computing. Physica-Verlag, A Springer-Verlag Company (2000)Google Scholar
  4. 4.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Matlab Curriculum Series. Prentice Hall, Englewood Cliffs (1997)Google Scholar
  5. 5.
    Kantz, H.: Noise reduction for experimental dataGoogle Scholar
  6. 6.
    Łȩski, J., Czogała, E.: A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications. Busefal 71, 72–81 (1997)Google Scholar
  7. 7.
    Łȩski, J., Czogała, E.: A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications. Fuzzy Sets and Systems 108(3), 289–297 (1999)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)CrossRefzbMATHGoogle Scholar
  9. 9.
    Nelles, O., Isermann, R.: Basis function networks for interpolation of local linear models. In: Proceedings of the 35th IEEE Conference on Decision and Control, vol. 1, pp. 470–475 (1996)Google Scholar
  10. 10.
    Rutkowski, L., Cpałka, K.: Flexible neuro-fuzzy systems. IEEE Transactions on Neural Networks 14(3), 554–574 (2003)CrossRefGoogle Scholar
  11. 11.
    Simiński, K.: Neuro-fuzzy system with hierarchical partition of input domain. Studia Informatica 29(4A (80)) (2008)Google Scholar
  12. 12.
    Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1), 15–33 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Systems, Man and Cybernetics 15(1), 116–132 (1985)CrossRefzbMATHGoogle Scholar
  14. 14.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80–83 (1945)CrossRefGoogle Scholar
  15. 15.
    Xiong, H., Pandey, G., Steinbach, M., Kumar, V.: Enhancing data analysis with noise removal. IEEE Transactions on Knowledge and Data Engineering 18(3), 304–319 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Krzysztof Simiński
    • 1
  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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