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A Flexible Connectionist Fuzzy System

  • Conference paper
Parallel Processing and Applied Mathematics (PPAM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3019))

Abstract

In this paper we derive flexible neuro-fuzzy systems based on Yager’s triangular norms. We incorporate various flexibility parameters into their construction. The parameters are learned by the standard recursive gradient procedures with constraints. The performance is illustrated on a typical approximation problem.

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References

  1. Gałkowski, T., Rutkowski, L.: Nonparametric fitting of multivariable functions. IEEE Transactions on Automatic Control AC-31, 785–787 (1986)

    Article  Google Scholar 

  2. Gałkowski, T., Rutkowski, L.: Nonparametric recovery of multivariate functions with applications to system identification. Proceedings of the IEEE 73, 942–943 (1985)

    Article  Google Scholar 

  3. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publishers, Netherlands (2000)

    MATH  Google Scholar 

  4. Nowicki, R., Rutkowski, L.: Rough-neuro-fuzzy system for classification. In: 9th International Conference on Neural Information Processing (ICONIP 2002), November 18-22, Orchid Country Club, Singapore (2002)

    Google Scholar 

  5. Nowicki, R., Rutkowski, L.: Soft techniques for bayesian classification. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks, and Soft Computing, pp. 537–544. Physica-Verlag, A Springer-Verlag Company, Heidelberg, New York (2003)

    Google Scholar 

  6. Nowicki, R., Scherer, R., Rutkowski, L.: A hierarchical neuro-fuzzy systems based on s-implication. In: IJCNN 2003 Conference Proceedings, International Joint Conference on Neural Networks, Portland, Oregano, July 20-24, pp. 321–325 (2003)

    Google Scholar 

  7. Nowicki, R., Scherer, R., Rutkowski, L.: A method for learning of hierarchical fuzzy systems. In: Proceedings of the 2nd Euro-International Symposium on Computational Intelligence, Koszyce, vol. 76, pp. 124–129 (2002)

    Google Scholar 

  8. Nowicki, R., Scherer, R., Rutkowski, L.: A neuro-fuzzy system based on the hierarchical prioritized structure. In: 10th Zittau Fuzzy Colloquium, Germany, September 4-6, pp. 192–198 (2002)

    Google Scholar 

  9. Rutkowska, D.: Neuro-Fuzzy Architectures and Hybrid Learning. Springer, Heidelberg (2001)

    Google Scholar 

  10. Rutkowska, D., Rutkowski, L.: Fuzzy neural networks. In: Proceedings of the Second International Conference on Parallel Processing, and Applied Mathematics, Zakopane, September 2-5, pp. 507–519 (1997)

    Google Scholar 

  11. Rutkowska, D., Rutkowski, L.: Neural-Fuzzy-Genetic Parallel Computing System as a Tool for Various Applications. In: Proceedings of the Third International Conference on Parallel Processing and Applied Mathematics (PPAM 1999), Kazimierz Dolny, pp. 489–498 (1999)

    Google Scholar 

  12. Rutkowska, D., Nowicki, R., Rutkowski, L.: Neuro-fuzzy architectures with various implication operators. In: Sincák, P., Vascak, J., Kvasnicka, V., Mesiar, R. (eds.) The State of the Art in Computational Intelligence, pp. 214–219. Physica-Verlag, A Springer-Verlag Company, Heidelberg, New York (2000)

    Google Scholar 

  13. Rutkowska, D., Nowicki, R., Rutkowski, L.: Singleton and Non-Singleton Fuzzy Systems with Nonparametric Defuzzification. In: Szczepaniak, P.S. (ed.) Computational Intelligence, and Applications, pp. 292–301. Springer, Heidelberg (1999)

    Google Scholar 

  14. Rutkowska, D., Piliński, M., Rutkowski, L.: Fuzzy neural controllers. In: Proceedings of Wismarer Automatisierungssymposium, Hochschule Wismar, September 17-18 (1996) H2-lH2-H8

    Google Scholar 

  15. Rutkowska, D., Rutkowski, L., Nowicki, R.: Fuzzy neural networks with nonparametric defuzzification. In: Proceedings of the 9-th International Conference, System-Modelling-Control, Zakopane, April 27 - May 1 (1998)

    Google Scholar 

  16. Rutkowska, D., Rutkowski, L., Nowicki, R.: On processing of noisy data by fuzzy inference neural networks. In: Proceedings of the IASTED International Conference Signal, and Image Processing (SIP 1999), Nassau, Bahamas, pp. 314–318 (1999)

    Google Scholar 

  17. Rutkowski, L.: Adaptive probabilistic neural-networks for pattern classification in time-varying environment. IEEE Trans. Neural Networks 15 (May 2004)

    Google Scholar 

  18. Rutkowski, L.: An application of multiple Fourier series to identification of multivariable nonstationary systems. International Journal of Systems Science 20(10), 1993–2002 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  19. Rutkowski, L.: Identification of MISO nonlinear regressions in the presence of a wide class of disturbances. IEEE Transactions on Information Theory IT-37, 214–216 (1991)

    Article  MathSciNet  Google Scholar 

  20. Rutkowski, L.: Multiple Fourier series procedures for extraction of nonlinear regressions from noisy data. IEEE Transactions on Signal Processing 41(10), 3062–3065 (1993)

    Article  MATH  Google Scholar 

  21. Rutkowski, L.: New Soft Computing Techniques for System Modelling. Pattern Classification and Image Processing. Springer-Verlag (2004)

    Google Scholar 

  22. Rutkowski, L., Cpałka, K.: A General Approach to Neuro-Fuzzy Systems. In: The 10th IEEE International Conference on Fuzzy Systems, Melbourne (2001)

    Google Scholar 

  23. Rutkowski, L., Cpałka, K.: Flexible neuro-fuzzy systems. IEEE Trans. Neural Networks 14, 554–574 (2003)

    Article  Google Scholar 

  24. Rutkowski, L., Gałkowski, T.: On pattern classification and system identification by probabilistic neural networks. Applied Mathematics, and Computer Science 4(3), 413–422 (1994)

    Google Scholar 

  25. Rutkowski, L., Piliński, M.: Neural networks for fuzzy control. In: Proceedings of the 8-th International Conference, System-Modelling-Control, pp. 96–98 (1995)

    Google Scholar 

  26. Rutkowski, L., Rafajłowicz, E.: On global rate of convergence of some nonparametric identification procedures. IEEE Transaction on Automatic Control AC-34(10), 1089–1091 (1989)

    Article  Google Scholar 

  27. Rutkowski, L., Rutkowska, D., Gałkowski, T.: Probabilistic neural networks and fuzzy logic systems. In: Proceedings of the 8-th International Conference, System- Modelling-Control, pp. 99–102 (1995)

    Google Scholar 

  28. Rutkowska, D., Nowicki, R., Rutkowski, L.: Neuro-Fuzzy System with Inference Process Based on Zadeh Implication. In: Proceedings of the Third International Conference on Parallel Processing and Applied Mathematics (PPAM 1999), Kazimierz Dolny, pp. 597–602 (1995)

    Google Scholar 

  29. Rutkowski, L., Starczewski, J.: From type-1 to type-2 fuzzy interference systems - part 1. In: Proceedings of the Fifth Conference Neural Networks, and Soft Computing, Zakopane, June 6-10, pp. 46–51 (2000)

    Google Scholar 

  30. Rutkowski, L., Starczewski, J.: From type-1 to type-2 fuzzy interference systems - part 2. In: Proceedings of the Fifth Conference Neural Networks, and Soft Computing, Zakopane, June 6-10, pp. 52–65 (2000)

    Google Scholar 

  31. Rutkowski, L., Zapart, K.: Fuzzy neural networks and their applications. In: Proceedings of the 8-th International Conference, System-Modelling-Control, pp. 41–46 (1995)

    Google Scholar 

  32. Scherer, R., Rutkowski, L.: A fuzzy relational system with linguistic antecedent certainty factors. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks, and Soft Computing, pp. 563–569. Physica-Verlag, A Springer-Verlag Company, Heidelberg, New York (2003)

    Google Scholar 

  33. Scherer, R., Rutkowski, L.: A neuro-fuzzy relational system. In: Fourth International Conference on Parallel Processing, and Applied Mathematics, Czestochowa, pp. 131–135 (2001)

    Google Scholar 

  34. Scherer, R., Rutkowski, L.: A survey of hierarchical fuzzy systems. In: Proceedings of the Fifth Conference Neural Networks, and Soft Computing, Zakopane, June 6-10, pp. 374–379 (2000)

    Google Scholar 

  35. Scherer, R., Rutkowski, L.: Neuro-fuzzy relational systems. In: 9th International Conference on Neural Information Processing (ICONIP 2002), November 18-22, Orchid Country Club, Singapore (2002)

    Google Scholar 

  36. Scherer, R., Rutkowski, L.: Relational equations initializing neuro-fuzzy system. In: 10th Zittau Fuzzy Colloquium, Germany, September 4-6, pp. 212–217 (2002)

    Google Scholar 

  37. Starczewski, J., Rutkowski, L.: Connectionist Structures of Type 2 Fuzzy Inference Systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2001. LNCS, vol. 2328, pp. 634–642. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  38. Starczewski, J., Rutkowski, L.: Interval type 2 neuro-fuzzy systems based on interval consequents. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks, and Soft Computing, pp. 570–577. Physica-Verlag, A Springer-Verlag Company, Heidelberg, New York (2003)

    Google Scholar 

  39. Starczewski, J., Rutkowski, L.: Neuro-fuzzy inference systems of type 2. In: 9th International Conference on Neural Information Processing (ICONIP 2002), November 18-22, Orchid Country Club, Singapore (2002)

    Google Scholar 

  40. Yager, R.R., Filev, D.P.: Essentials of Fuzzy Modeling and Control. John Wiley and Sons, Chichester (1994)

    Google Scholar 

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Author information

Authors and Affiliations

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

    Krzysztof Cpałka

Authors
  1. Krzysztof Cpałka
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Editor information

Editors and Affiliations

  1. Institute of Computational and Information Sciences, Czestochowa University of Technology,  

    Roman Wyrzykowski

  2. Computer Science Department, University of Tennessee, TN 37996-3450, Knoxville, USA

    Jack Dongarra

  3. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Marcin Paprzycki

  4. Informatics & Mathematical Modeling, Technical University of Denmark, DK-2800, Lyngby, Denmark

    Jerzy Waśniewski

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Cpałka, K. (2004). A Flexible Connectionist Fuzzy System. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2003. Lecture Notes in Computer Science, vol 3019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_81

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  • DOI: https://doi.org/10.1007/978-3-540-24669-5_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21946-0

  • Online ISBN: 978-3-540-24669-5

  • eBook Packages: Springer Book Archive

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