Abstract
The presented algorithm of fuzzy back propagation used in the teaching phase of a neural fuzzy controller combines the best features of two selected soft computing elements: fuzzy logic and the selected structure of artificial neural network. This approach is the expansion of the classical algorithm of back propagation by use of not only binary, but also any values from the range [0...1] in the teaching sequences and the selection of the value of the teaching factor η using the theory of fuzzy sets. © Springer-Verlag 2001.
Cite
CITATION STYLE
Bedzak, M. (2001). Fuzzy-η for back propagation networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2206 LNCS, pp. 86–91). Springer Verlag. https://doi.org/10.1007/3-540-45493-4_13
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