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Abstract
The paper presents novel modeling of fuzzy inference system by using the ‘fuzzified’ radial basis function (RBF) neural network (NN). RBF NN performs the mapping of the antecedent fuzzy numbers (a.k.a. membership functions, attributes, possibilities degrees) into the consequent ones. In this way, an RBF NN is capable of performing the rigorous calculus with fuzzy numbers. Prior the mapping, both the antecedents and the consequents are discretized and transferred into the ndimensional and mdimensional ‘fuzzy’ vectors. These vectors present the training inputs and outputs of an RBF NN and, in this way, an RBF network performs an exact R n → R m mapping. The generalization capacity of such a neural implementation is superior to the ability of the original fuzzy model.
Original language  English 

Title of host publication  Advances in soft computing 
Subtitle of host publication  Neural Networks and Soft Computing 
Editors  Leszek Rutkowski, Jaunsz Kacprzyk 
Publisher  Springer Heidelberg 
Chapter  Advances in Soft Computing book series, Vol. 19 
Pages  516522 
Volume  19 
ISBN (Electronic)  9783790819021 
ISBN (Print)  9783790800050 
Publication status  Published  2003 
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Dive into the research topics of 'Fuzzy Calculus by RBF Neural Networks'. Together they form a unique fingerprint.Activities
 1 Peer review responsibility, including review panel or committee

Mathematical and Compuational Applications (Journal)
Li, Z. (Reviewer)
01 Sept 2023 → 30 Oct 2023Activity: Publication peerreview and editorial work › Peer review responsibility, including review panel or committee