Improvement of Precision of Neuro-Fuzzy System by Increase of Activation of Rules

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 613)

Abstract

Neuro-fuzzy systems have proved to be a powerful tool for data approximation and generalization. A rule base is a crucial part of a neuro-fuzzy system. The data items activate the rules and their answers are aggregated into a final answer. The experiments reveal that sometimes the activation of all rules in a rule base is very low. It means the system recognizes the data items very poorly. The paper presents a modification of the neuro-fuzzy system: the tuning procedure has two objectives: minimizing of the error of the system and maximizing of the activation of rules. The higher activation (better recognition of the data items) makes the model more reliable. The increase of the activation of rules may also decrease the error rate for the model. The paper is accompanied by the numerical examples.

Keywords

Neuro-fuzzy system Activation of rules 

References

  1. 1.
    Alcalá-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult.-Valued Log. Soft Comput. 17(2–3), 255–287 (2011)Google Scholar
  2. 2.
    Bezerra, R.A., Vellasco, M.M., Tanscheit, R.: Hierarchical neuro-fuzzy BSP Mamdani system. In: Neural Networks, Genetic Algorithms and Soft Computing, pp. 1321–1326 (2005)Google Scholar
  3. 3.
    Czabański, R.: Extraction of fuzzy rules using deterministic annealing integrated with \(\epsilon \)-insensitive learning. Int. J. Appl. Math. Comput. Sci. 16(3), 357–372 (2006)MathSciNetMATHGoogle Scholar
  4. 4.
    Czogała, E., Łęski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Studies in Fuzziness and Soft Computing. Physica-Verlag, Heidelberg (2000)CrossRefMATHGoogle Scholar
  5. 5.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters. J. Cybern. 3(3), 32–57 (1973)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository (2010)Google Scholar
  7. 7.
    Jakubek, S., Keuth, N.: A local neuro-fuzzy network for high-dimensional models and optimalization. Eng. Appl. Artif. Intell. 19(6), 705–717 (2006)CrossRefGoogle Scholar
  8. 8.
    Łęski, J.: \(\varepsilon \)-insensitive learing techniques for approximate reasoning systems. Int. J. Comput. Cogn. 1(1), 21–77 (2003)Google Scholar
  9. 9.
    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 Syst. 108(3), 289–297 (1999)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Mamdani, E.H.: Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. C–26(12), 1182–1191 (1977)CrossRefMATHGoogle Scholar
  11. 11.
    Senhadji, R., Sanchez-Solano, S., Barriga, A., Baturone, I., Moreno-Velo, F.: Norfrea: an algorithm for non redundant fuzzy rule extraction. IEEE Int. Conf. Syst. Man Cybern. 1, 604–608 (2002)CrossRefGoogle Scholar
  12. 12.
    Setnes, M., Babuška, R.: Rule base reduction: some comments on the use of orthogonal transforms. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 31(2), 199–206 (2001)CrossRefGoogle Scholar
  13. 13.
    Sikora, M., Krzystanek, Z., Bojko, B., Śpiechowicz, K.: Application of a hybrid method of machine learning for description and on-line estimation of methane hazard in mine workings. J. Min. Sci. 47(4), 493–505 (2011)CrossRefGoogle Scholar
  14. 14.
    Simiński, K.: Patchwork neuro-fuzzy system with hierarchical domain partition. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 11–18. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Simiński, K.: Neuro-fuzzy system based kernel for classification with support vector machines. In: Gruca, A., Czachórski, T., Kozielski, S. (eds.) Man-Machine Interactions 3. AISC, vol. 242, pp. 415–422. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  16. 16.
    Simiński, K.: Rough subspace neuro-fuzzy system. Fuzzy Sets Syst. 269, 30–46 (2015). http://www.sciencedirect.com/science/article/pii/S0165011414003108
  17. 17.
    Siminski, K.: Ridders algorithm in approximate inversion of fuzzy model with parameterized consequences. Expert Syst. Appl. 51, 276–285 (2016)CrossRefGoogle Scholar
  18. 18.
    Sugeno, M., Tanaka, K.: Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets Syst. 42(3), 315–334 (1991)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1(1), 7–31 (1993)CrossRefGoogle Scholar
  20. 20.
    Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797–1808 (1998)CrossRefGoogle Scholar
  21. 21.
    Zhou, Z.H., Chen, S.F.: Rule extraction from neural networks. J. Comput. Res. Dev. 39(4), 398–405 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

Personalised recommendations