Abstract
In the paper, we present a new fuzzy relational system with multiple outputs for classification purposes. Rules in the system are more flexible than the rules in linguistic fuzzy systems because of the additional weights in rule consequents. The weights comes 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 for a given class. Simulation results confirmed the system ability to classify data.
Cite
CITATION STYLE
Scherer, R., & Rutkowski, L. (2004). Neuro-fuzzy relational classifiers. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3070, pp. 376–380). Springer Verlag. https://doi.org/10.1007/978-3-540-24844-6_54
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