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Evolutionary Learning for Neuro-fuzzy Ensembles with Generalized Parametric Triangular Norms

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Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

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Abstract

In this paper we present a method for designing neuro-fuzzy systems with Mamdani-type inference and parametric t-norm connecting rule antecedents. Hamacher product was used as t-norm. The neuro-fuzzy systems are used to create an ensemble of classifiers. After obtaining the ensemble by bagging, every neuro-fuzzy system has its t-norm parameters fine-tuned. Thanks to this the accuracy is improved and the number of parameters can be reduced. The proposed method is tested on a well known benchmark.

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References

  1. Breiman, L.: Bagging predictors. Machine Learning 26(2), 123–140 (1996)

    Google Scholar 

  2. Cordon, O., Herrera, F., Hoffman, F., Magdalena, L.: Genetic Fuzzy System, Evolutionary Tunning and Learning of Fuzzy Knowledge Bases. World Scientific, Singapore (2000)

    Google Scholar 

  3. Cordon, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy sets and systems 141, 5–31 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  4. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  5. Gabryel, M., Cpalka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: I Workshop on Genetic Fuzzy Systems, Granada, pp. 119–123 (2005)

    Google Scholar 

  6. Gabryel, M., Rutkowski, L.: Evolutionary Learning of Mamdani-type Neuro-Fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 354–359. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Gabryel, M., Rutkowski, L.: Evolutionary Methods for Designing Neuro-fuzzy Modular Systems Combined by Bagging Algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 398–404. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Korytkowski, M., Gabryel, M., Rutkowski, L., Drozda, S.: Evolutionary Methods to Create Interpretable Modular System. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 405–413. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Kuncheva, L.I.: Fuzzy Classifier Design. Physica Verlag, Heidelberg (2000)

    MATH  Google Scholar 

  10. Klement, E.P., Mesiar, R., Pap, E.: Triangular norms. Position paper II: general constructions and parametrized families. Fuzzy Sets and Systems 145, 411–438 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  11. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)

    MATH  Google Scholar 

  12. Rutkowska, D., Nowicki, R.: Implication-Based Neuro-Fuzzy Architectures. Intenrational Journal of Applied Mathematics and Computer Science 10(4) (2000)

    Google Scholar 

  13. Rutkowska, D.: Neuro Fuzzy Architectures and Hybrid Learning. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  14. Rutkowski, L.: Computational Inteligence, Methods and Techniques. Springer, Heidelberg (2008)

    Google Scholar 

  15. Rutkowski, L.: Flexible Neuro Fuzzy Systems. Kluwer Academic Publishers, Dordrecht (2004)

    MATH  Google Scholar 

  16. Mertz, C.J., Murphy, P.M.: UCI respository of machine learning databases, http://www.ics.uci.edu/pub/machine-learning-databases

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Authors and Affiliations

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

    Marcin Gabryel, Marcin Korytkowski & Rafał Scherer

  2. The Professor Kotarbinski Olsztyn Academy of Computer Science and Management, ul. Artyleryjska 3c, 10-165, Olsztyn, Poland

    Marcin Gabryel & Marcin Korytkowski

  3. The Faculty of Mathematics and Computer Sciences, University of Warmia and Mazury in Olsztyn, ul. Zolnierska 14, 10-561, Olsztyn, Poland

    Stanisław Drozda

  4. Institute of Mathematics and Computer Science, Jan Długosz University, al. Armii Krajowej 13/15, 42-200, Czȩstochowa, Poland

    Agata Pokropinska

  5. Institute of Information Technology, Academy of Management (SWSPiZ), ul. Sienkiewicza 9, 90-113, Łódź, Poland

    Rafał Scherer

Authors
  1. Marcin Gabryel
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  2. Marcin Korytkowski
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  3. Agata Pokropinska
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  4. Rafał Scherer
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  5. Stanisław Drozda
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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Gabryel, M., Korytkowski, M., Pokropinska, A., Scherer, R., Drozda, S. (2010). Evolutionary Learning for Neuro-fuzzy Ensembles with Generalized Parametric Triangular Norms. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_10

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  • DOI: https://doi.org/10.1007/978-3-642-13208-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

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