Abstract
RBF neural networks are an efficient tool for acquisition and representation of functional relations reflected in empirical data. The interpretation of acquired knowledge is, however, generally difficult because the knowledge is encoded into values of the parameters of the network. Contrary to neural networks, fuzzy systems allow a more convenient interpretation of the stored knowledge in the form of IF-THEN rules. This paper contributes to the fusion of these two concepts. Namely, we show that a RBF neural network can be interpreted as the radial fuzzy system. The proposed approach is based on the study of conjunctive and implicative representations of the rule base in radial fuzzy systems. We present conditions under which both representations are computationally close and, as the consequence, a reasonable syntactic interpretation of RBF neural networks can be introduced.
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© 2015 Springer International Publishing Switzerland
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Coufal, D. (2015). RBF Neural Networks and Radial Fuzzy Systems. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_20
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DOI: https://doi.org/10.1007/978-3-319-23983-5_20
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-23983-5
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