RBF Neural Networks and Radial Fuzzy Systems

0453637 - ÚI 2016 RIV CH eng C - Conference Paper (international conference)
Coufal, David
RBF Neural Networks and Radial Fuzzy Systems.
Engineering Applications of Neural Networks. Cham: Springer, 2015 - (Iliadis, L.; Jayne, C.), s. 206-215. Communications in Computer and Information Science, 517. ISBN 978-3-319-23981-1. ISSN 1865-0929. E-ISSN 1865-0937.
[EANN 2015. International Conference /16./. Rhodes (GR), 25.09.2015-28.09.2015]
R&D Projects: GA MŠMT(CZ) LD13002
Institutional support: RVO:67985807
Keywords : RBF neural networks * Radial fuzzy systems * Conjunctive and implicative rule bases
Subject RIV: IN - Informatics, Computer Science

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.
Permanent Link: http://hdl.handle.net/11104/0254403