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International Conference on Computational Intelligence

Fuzzy Days 1997: Computational Intelligence Theory and Applications pp 35–45Cite as

Structure identification of functional-type fuzzy models with application to modelling nonlinear dynamic plants

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1226))

Abstract

A new fuzzy model structure identification method, based on orthogonalisation and statistical tests, as well as information criteria to obtain a minimum rule base and a minimum number of membership functions from input-output data, is proposed. The method is applied to functional-type fuzzy models. The applicability of the proposed method to nonlinear static and dynamic systems is illustrated by examples.

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Author information

Authors and Affiliations

  1. Control Engineering Laboratory, Faculty of Electrical Engineering, Ruhr-University Bochum, D-44780, Bochum

    P. Kortmann & H. Unbehauen

Authors
  1. P. Kortmann
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  2. H. Unbehauen
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Editor information

Bernd Reusch

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© 1997 Springer-Verlag Berlin Heidelberg

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Kortmann, P., Unbehauen, H. (1997). Structure identification of functional-type fuzzy models with application to modelling nonlinear dynamic plants. In: Reusch, B. (eds) Computational Intelligence Theory and Applications. Fuzzy Days 1997. Lecture Notes in Computer Science, vol 1226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62868-1_95

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  • DOI: https://doi.org/10.1007/3-540-62868-1_95

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62868-2

  • Online ISBN: 978-3-540-69031-3

  • eBook Packages: Springer Book Archive

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