Abstract
In the paper a boosting ensemble of neuro-fuzzy relational systems is created. Rules in relational fuzzy systems are more flexible than rules in linguistic fuzzy systems because of the additional weights in rule consequents. The weights come from an additional binary relation. Thanks to this, input and output fuzzy sets are related to each other with a certain degree. The size of the relations is determined by the number of input fuzzy sets and the number of output fuzzy sets. Simulations performed on popular benchmarks show that the proposed ensemble outperforms other learning systems.
This work was supported in part by the Foundation for Polish Science (Professorial Grant 2005-2008) and the Polish State Committee for Scientific Research (Grant Nr T11C 04827).
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Scherer, R. (2006). Boosting Ensemble of Relational Neuro-fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_33
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DOI: https://doi.org/10.1007/11785231_33
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