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Data Noise Reduction in Neuro-fuzzy Systems

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

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.

Keywords

Fuzzy Inference System Noise Removal Isosceles Triangle Data Noise Discrete Convolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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