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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© 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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