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Rough Sets-Based Recursive Learning Algorithm for Radial Basis Function Networks

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Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

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Abstract

A recursive learning algorithm based on the rough sets approach to parameter estimation for radial basis function neural networks is proposed. The algorithm is intended for the pattern recognition and classification problems. It can also be applied to neuro control, identification, and emulation.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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References

  1. Abe, S.: Neural Networks and Fuzzy Systems. Kluwer Academic Publishers, Boston (1997)

    MATH  Google Scholar 

  2. Haykin, S.: Neural Networks. A Comprehensive Foundation. Prentice Hall, Inc., Upper Saddle River (1999)

    MATH  Google Scholar 

  3. Ruan, D. (ed.): Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms. Kluwer Academic Publishers, Boston (1997)

    MATH  Google Scholar 

  4. Schweppe, F.C.: Uncertain Dynamic Systems. Prentice-Hall, Englewood Cliffs (1973)

    Google Scholar 

  5. Norton, J.P.: An Introduction to Identification. Academic Press Inc., London (1986)

    MATH  Google Scholar 

  6. Pawlak, Z.: Rough sets present state and further prospects. In: Proc. Int. Workshop on Rough Sets and Soft Computing, San Jose, California, pp. 72–76 (1994)

    Google Scholar 

  7. Yasdi, R.: Combining rough sets learning and neural learning method to deal with uncertain and imprecise information. Neurocomputing 7, 61–84 (1995)

    Article  MATH  Google Scholar 

  8. Fogel, E., Huang, Y.F.: On the value of information in system identification — bounded noise case. Automatics 18(2), 229–238 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  9. Halwass, M.: “Least-Squares”-Modificationen in Gegenwart begrenzter Stoerungen. MSR 33(8), 351–355 (1990)

    Google Scholar 

  10. Hägglund, J.: Recursive identification of slowly time-varying parameters. In: Proc. IFAC/IFORS Symp. on Identification and System Parameters Estimation, York, UK, pp. 1137–1142 (1985)

    Google Scholar 

  11. Prechelt, L.: Proben1 – a set of neural network problems and benchmarking rules. Technical Report 21/94, Fakultät für Informatik, Universität Karlsruhe (September 1994)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Control Systems Research Laboratory, Kharkiv National University of Radioelectronics, 14, Lenin Av., Kharkiv, 61166, Ukraine

    Yevgeniy Bodyanskiy, Yevgen Gorshkov, Vitaliy Kolodyazhniy & Irina Pliss

Authors
  1. Yevgeniy Bodyanskiy
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  2. Yevgen Gorshkov
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  3. Vitaliy Kolodyazhniy
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  4. Irina Pliss
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

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

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Bodyanskiy, Y., Gorshkov, Y., Kolodyazhniy, V., Pliss, I. (2005). Rough Sets-Based Recursive Learning Algorithm for Radial Basis Function Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_10

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  • DOI: https://doi.org/10.1007/11550907_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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