Advertisement

Merging Strategy for Local Model Networks Based on the Lolimot Algorithm

  • Torsten Fischer
  • Oliver Nelles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

In this paper an extension of the established training algorithm for nonlinear system identification called Lolimot is presented [9]. It is a heuristic tree-construction method that trains a local linear neuro-fuzzy network. Due to its very simple partitioning strategy, Lolimot is a fast and robust modeling approach, but has a limited flexibility. Therefore a new merging approach for regression tasks is presented, that can rearrange the local model structure in the input space, without harming the global model complexity.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bache, K., Lichman, M.: UCI machine learning repository (2013)Google Scholar
  2. 2.
    Breiman, L., Stone, C.J., Friedman, J.H., Olshen, R.: Classification and Regression Trees. Chapman and Hall, Boca Raton (1984)zbMATHGoogle Scholar
  3. 3.
    Burnham, K.P., Anderson, D.R.: Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer, New York (2002)Google Scholar
  4. 4.
    Gasso, K., Mourot, G., Ragot, J.: Structure identification in multiple model representation: Elimination and merging of local models. In: Proceedings of the 40th IEEE Conference on Decision and Control, vol. 3, pp. 2992–2997 (2001)Google Scholar
  5. 5.
    Gasso, K., Mourot, G., Ragot, J.: Structure identification of multiple models with output error local models. In: Proceedings of 15th IFAC World Congress on Automatic Control, vol. M, pp. 151–156 (2002)Google Scholar
  6. 6.
    Delgado, M., Gómez-Skarmeta, A.F.: M.A. Villa. About the use of fuzzy clustering for fuzzy model identification. Fuzzy Sets and Systems 106(2), 179–188 (1999)CrossRefGoogle Scholar
  7. 7.
    Johansen, T.A., Foss, B.A.: Identification of non-linear system structure and parameters using regime decomposition. Automatica 31(2), 321–326 (1995)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Kaymak, U., Babuska, R.: Compatible cluster merging for fuzzy modelling. In: International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, Fuzzy Systems, vol. 2, pp. 897–904 (1995)Google Scholar
  9. 9.
    Nelles, O.: Nonlinear System Identification: From Classical Approach to Neural Networks and Fuzzy Models. Springer, Berlin (2001)CrossRefGoogle Scholar
  10. 10.
    Nelles, O.: Axes-oblique partitioning strategies for local model networks. In: Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, pp. 2378–2383. IEEE (2006)Google Scholar
  11. 11.
    Nelles, O., Sinsel, S., Isermann, R.: Local basis function networks for identification of a turbocharger. In: Control 1996, UKACC International Conference on (Conf. Publ. No. 427), vol. 1, pp. 7–12 (1996)Google Scholar
  12. 12.
    Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets and Systems 28(1), 15–33 (1988)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Torsten Fischer
    • 1
  • Oliver Nelles
    • 1
  1. 1.Department of Mechanical EngineeringUniversity SiegenSiegenGermany

Personalised recommendations