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Simplification of Neuro-Fuzzy Models

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 59))

Abstract

The neuro-fuzzy system presented in the paper is a system with parameterized consequences implementing hierarchical partition of the input domain. The regions are described with attributes values. In this system not all attribute values must be used to constitute the region. The attributes of minor importance may be ignored. The results of experiments show that the simplified model have less parameters and can achieve better generalisation ability.

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References

  1. Almeida, M.R.A.: Sistema híbrido neuro-fuzzy-genético para mineração automática de dados. Master’s thesis, Pontifíca Universidade Católica do Rio de Janeiro (2004)

    Google Scholar 

  2. Chiu, S.L.: Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2, 267–278 (1994)

    Article  Google Scholar 

  3. Czabański, R.: Neuro-fuzzy modelling based on a deterministic annealing approach. International Journal of Applied Mathematics and Computer Science 15(4), 561–576 (2005)

    MATH  MathSciNet  Google Scholar 

  4. Czekalski, P.: Evolution-fuzzy rule based system with parameterized consequences. International Journal of Applied Mathematics and Computer Science 16(3), 373–385 (2006)

    MATH  MathSciNet  Google Scholar 

  5. Czogała, E., Łęski, J.: Fuzzy and neuro-fuzzy intelligent systems. Studies in Fuzziness and Soft Computing (2000)

    Google Scholar 

  6. Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters. Journal Cybernetics 3(3), 32–57 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  7. Jang, J.S.R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23, 665–684 (1993)

    Article  Google Scholar 

  8. Larminat, P., Thomas, Y.: Automatyka – układy liniowe. Wydawnictwa Naukowo-Techniczne (1983)

    Google Scholar 

  9. Łęski, J., Czogała, E.: A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications. BUSEFAL 71, 72–81 (1997)

    Google Scholar 

  10. Łęski, J.: Systemy neuronowo-rozmyte. Wydawnictwa Naukowo-Techniczne. Warsaw, Poland (2008)

    Google Scholar 

  11. Łęski, J., Czogała, E.: A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications. Fuzzy Sets and Systems 108(3), 289–297 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  12. Makridakis, S.G., Wheelwright, S.C., Hyndman, R.J.: Forecasting: Methods and Applications, 3rd edn. John Wiley & Sons, Chichester (1998)

    Google Scholar 

  13. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)

    Article  MATH  Google Scholar 

  14. Nelles, O., Fink, A., Babuška, R., Setnes, M.: Comparison of two construction algorithms for Takagi-Sugeno fuzzy models. International Journal of Applied Mathematics and Computer Science 10(4), 835–855 (2000)

    MATH  Google Scholar 

  15. Nelles, O., Isermann, R.: Basis function networks for interpolation of local linear models. In: Proceedings of the 35th IEEE Conference on Decision and Control, vol. 1, pp. 470–475 (1996)

    Google Scholar 

  16. Pedrycz, W.: Conditional fuzzy clustering in the design of radial basis function neural networks. IEEE Transactions on Neural Networks 9(4), 601–612 (1998)

    Article  Google Scholar 

  17. Rutkowski, L., Cpałka, K.: Flexible neuro-fuzzy systems. IEEE Transactions on Neural Networks 14(3), 554–574 (2003)

    Article  Google Scholar 

  18. Simiński, K.: Neuro-fuzzy system with hierarchical partition of input domain. Studia Informatica 29(4A (80)), 43–53 (2008)

    Google Scholar 

  19. Simiński, K.: Two ways of domain partition in fuzzy inference system with parametrized consequences: Clustering and hierarchical split. In: Proceedings of the 10th International PhD Workshop, pp. 103–108 (2008)

    Google Scholar 

  20. de Souza, F.J., Vellasco, M.M.R., Pacheco, M.A.C.: Hierarchical neuro-fuzzy quadtree models. Fuzzy Sets and Systems 130(2), 189–205 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  21. Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets and Systems 28(1), 15–33 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  22. Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems 1(1), 7–31 (1993)

    Article  Google Scholar 

  23. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 15(1), 116–132 (1985)

    MATH  Google Scholar 

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Simiński, K. (2009). Simplification of Neuro-Fuzzy Models. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_27

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  • DOI: https://doi.org/10.1007/978-3-642-00563-3_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00562-6

  • Online ISBN: 978-3-642-00563-3

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