Summary
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
Czogała, E., Łȩski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Series in Fuzziness and Soft Computing. Physica-Verlag, A Springer-Verlag Company (2000)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Matlab Curriculum Series. Prentice Hall, Englewood Cliffs (1997)
Kantz, H.: Noise reduction for experimental data
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)
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)
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)
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)
Rutkowski, L., Cpałka, K.: Flexible neuro-fuzzy systems. IEEE Transactions on Neural Networks 14(3), 554–574 (2003)
Simiński, K.: Neuro-fuzzy system with hierarchical partition of input domain. Studia Informatica 29(4A (80)) (2008)
Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1), 15–33 (1988)
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)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80–83 (1945)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Simiński, K. (2009). Data Noise Reduction in Neuro-fuzzy Systems. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-93905-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-93904-7
Online ISBN: 978-3-540-93905-4
eBook Packages: EngineeringEngineering (R0)