Fuzzy Calculus by RBF Neural Networks

  • Vojislav Kecman
  • Zhenquan Li
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)


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 n-dimensional and m-dimensional ‘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.


Membership Function Radial Basis Function Fuzzy Number Fuzzy Model Inference Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Vojislav Kecman
    • 1
  • Zhenquan Li
    • 1
  1. 1.School of EngineeringUniversity of AucklandAucklandNew Zealand

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