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An algorithm for extracting fuzzy rules based on RBF neural network
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DOI：10.1109/TIE.2006.878305.png)
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Abstract
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A four-layer fuzzy-neural network structure and some algorithms for extracting fuzzy rules from numeric data by applying the functional equivalence between radial basis function (RBF) networks,and a simplified class of fuzzy inference systems are proposed. The RBF neural network not only expresses the architecture of fuzzy systems clearly but also maintains the explanative characteristic of linguistic meaning. The fuzzy partition algorithm of input space, inference algorithm, and parameter tuning algorithm are also discussed. Simulation examples are given to illustrate the validity of the proposed algorithms.
Keywords:
explanative characteristic
fuzzy rules
radial basis function (RBF) neural network
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