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Compromise Weighted Neuro — Fuzzy Systems

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Neural Networks and Soft Computing

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

Summary

In the paper we present new neuro — fuzzy systems. They are called the AND-type fuzzy inference systems (NFIS). Based on the input — output data we learn not only parameters of membership functions but also a type of the systems and aggregating parameters. We propose the weighted T-norm and S-norm to neuro — fuzzy inference systems.

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References

  1. E. P. Klement, R. Mesiar, E. Pap, Triangular Norms, Kluwer Academic Publishers, Netherlands 2000.

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  2. J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice Hall PTR, Upper Saddle River, NJ, 2001.

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Author information

Authors and Affiliations

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

    Leszek Rutkowski & Krzysztof Cpałka

Authors
  1. Leszek Rutkowski
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  2. Krzysztof Cpałka
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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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Rutkowski, L., Cpałka, K. (2003). Compromise Weighted Neuro — Fuzzy Systems. 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_85

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  • DOI: https://doi.org/10.1007/978-3-7908-1902-1_85

  • 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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