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.
This work was supported in part by the Foundation for Polish Science (Professorial Grant 2005-2008) and the Polish Ministry of Science and Higher Education (Special Research Project 2006-2009) and by science funds for 2007-2010 as research project Nr N N516 1155 33.
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Starczewski, J., Scherer, R., Korytkowski, M., Nowicki, R. (2008). Modular Type-2 Neuro-fuzzy Systems. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2007. Lecture Notes in Computer Science, vol 4967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68111-3_59
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DOI: https://doi.org/10.1007/978-3-540-68111-3_59
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