A New Algorithm for Online Management of Fuzzy Rules Base for Nonlinear Modeling

  • Krystian ŁapaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 521)


In this paper a new algorithm for online management of fuzzy rules base for nonlinear modeling is proposed. The online management problem is complex due to limitations of memory and time needed for calculations. The proposed algorithm allows an online creation and management of fuzzy rules base. It is distinguished, among the others, by mechanisms of: managing of number of fuzzy rules, managing of fuzzy rules weights and possibilities of background learning. The proposed algorithm was tested on typical nonlinear modeling problems.


Neuro-fuzzy system Evolving fuzzy system Background learning 



The project was financed by the National Science Centre (Poland) on the basis of the decision number DEC-2012/05/B/ST7/02138.


  1. 1.
    Cpałka, K., Łapa, K., Przybył, A., Zalasiński, M.: A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects. Neurocomputing 135, 203–217 (2014)CrossRefGoogle Scholar
  2. 2.
    Gacto, M.J., Alcalá, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181(20), 4340–4360 (2011)CrossRefzbMATHGoogle Scholar
  3. 3.
    Kaisler, S., Armour, F., Espinosa, J.A., Money, W.: Big data: issues and challenges moving forward, system sciences (HICSS). In: Proceedings of the 46th Hawaii International Conference on, IEEE, pp. 995–1004 (2013)Google Scholar
  4. 4.
    Kibler, D., Aha, D.: Instance-based prediction of real-valued attributes. In Proceedings of the CSCSI (Canadian AI) Conference (1988)Google Scholar
  5. 5.
    Łapa, K., Cpałka, K.: On the application of a hybrid genetic-firework algorithm for controllers structure and parameters selection. Adv. Intel. Syst. Comput. 429, 111–123 (2016)CrossRefGoogle Scholar
  6. 6.
    Lughofer, E.: Evolving Fuzzy Systems—Methodologies. Advanced Concepts and Applications. Springer, Berlin (2011)CrossRefzbMATHGoogle Scholar
  7. 7.
    Lughofer, E.: On-line assurance of interpretability criteria in evolving fuzzy systems—achievements, new concepts and open issues. Inf. Sci. 251, 22–46 (2013)CrossRefGoogle Scholar
  8. 8.
    Lughofer, E., Hüllermeier, E.: On-line redundancy elimination in evolving fuzzy regression models using a fuzzy inclusion measure. In: Proceedings of the EUSFLAT 2011 Conference, Elsevier, Aix-Les-Bains, France, pp. 380–387 (2011)Google Scholar
  9. 9.
    Maciel, L., Lemos, A., Gomide, F., Ballini, R.: Evolving fuzzy systems for pricing fixed income options. Evolv. Syst. 3(1), 5–18 (2012)CrossRefGoogle Scholar
  10. 10.
    Rutkowski, L.: Computational Intelligence. Springer, Berlin (2008)CrossRefzbMATHGoogle Scholar
  11. 11.
    Rutkowski, L., Cpałka, K.: Flexible weighted neuro-fuzzy systems. In: Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’02), Orchid Country Club, Singapore, November 18–22 (2002)Google Scholar
  12. 12.
    Rutkowski, L., Cpałka, K.: Neuro-fuzzy systems derived from quasi-triangular norms. In: IEEE International Conference on Fuzzy Systems, Budapest, July 26–29, vol. 2, pp. 1031–1036 (2004)Google Scholar
  13. 13.
    Soleimani, H., Lucas, K., Araabi, B.N.: Recursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modelling. Evolv. Syst. 1(1), 59–71 (2010)CrossRefGoogle Scholar
  14. 14.
    Sugeno, M., Yasukawa, T.: A fuzzy logic based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1, 7–31 (1993)CrossRefGoogle Scholar
  15. 15.
    Thrun, S.: Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Kluwer Academic Publishers, Boston (1996)CrossRefzbMATHGoogle Scholar
  16. 16.
    Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Statist. Assoc. 58, 236–244 (1963)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Waugh, S.: Extending and benchmarking Cascade-Correlation. PhD thesis, Computer Science Department, University of Tasmania (1995)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Computational IntelligenceCzęstochowa University of TechnologyCzęstochowaPoland

Personalised recommendations