Abstract
In the paper we study a modular system which can be converted into a type-2 neuro-fuzzy system. The rule base of such system consists of triangular type-2 fuzzy sets. The modular structure is trained using the backpropagation method combined with the AdaBoost algorithm. By applying the type-2 neuro-fuzzy system, the modular structure is converted into a compressed form. This allows to overcome the training problem of type-2 neuro-fuzzy systems. An illustrative example is given to show the efficiency of our approach in the problems of classification. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Starczewski, J., Scherer, R., Korytkowski, M., & Nowicki, R. (2008). Modular type-2 neuro-fuzzy systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4967 LNCS, pp. 570–578). https://doi.org/10.1007/978-3-540-68111-3_59
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