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Membership Functions as Combination of Expert’s Knowledge with Population Information

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Neural Networks and Soft Computing

Part of the book series: Advances in Soft Computing ((AINSC,volume 19))

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Abstract

The paper aims at recommending an interpretation of medical indices with fuzzy rules. Advantages of expert’s knowledge combination with population information are observed. A simple method of performance estimation of fuzzy diagnosis support systems by a change of rules firing threshold is proposed. Results of three methods of membership function determination are compared. Conclusions can be useful indications for membership function defining.

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References

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Author information

Authors and Affiliations

  1. Div. of Biomedical Engineering, Institute of Electronics, Silesian University of Technology, 16 Akademicka St., 44-100, Gliwice, Poland

    Ewa Straszecka

  2. Department of Internal Diseases, Medical University of Silesia, 102 Edukacji St., 43-100, Tychy, Poland

    Joanna Straszecka

Authors
  1. Ewa Straszecka
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  2. Joanna Straszecka
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Technical University Częstochowa, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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© 2003 Springer-Verlag Berlin Heidelberg

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Straszecka, E., Straszecka, J. (2003). Membership Functions as Combination of Expert’s Knowledge with Population Information. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_47

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  • DOI: https://doi.org/10.1007/978-3-7908-1902-1_47

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0005-0

  • Online ISBN: 978-3-7908-1902-1

  • eBook Packages: Springer Book Archive

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