RBF Neural Networks and Radial Fuzzy Systems

Autoři: RNDr. Ing. David Coufal, Ph.D. et Ph.D.
Rok vydání: 2015
Typ publikace: Konferenční příspěvek (mezinárodní konf.)
Periodikum: Engineering Applications of Neural Networks
Projekty: Modelování složitých systémů softcomputingovými metodami
Bibliografická citace: COUFAL, David. RBF Neural Networks and Radial Fuzzy Systems. In: ILIADIS, L., JAYNE, C., eds. Engineering Applications of Neural Networks. Cham: Springer, 2015, S. 206-215. Communications in Computer and Information Science, 517. ISBN 978-3-319-23981-1. ISSN 1865-0929. E-ISSN 1865-0937
Dostupné z: doi:10.1007/978-3-319-23983-5_20
DOI: 10.1007/978-3-319-23983-5_20
ID: 453637

Abstrakt

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.