A low-complexity fuzzy activation function for artificial neural networks

Authors:
E. Soria-Olivas;J. D. Martin-Guerrero;G. Camps-Valls;A. J. Serrano-Lopez;J. Calpe-Maravilla;L. Gomez-Chova
Affiliations:
Dept. of Enginyeria Electronica, Univ. de Valencia, Spain;-;-;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2003

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Abstract

A novel fuzzy-based activation function for artificial neural networks is proposed. This approach provides easy hardware implementation and straightforward interpretability in the basis of IF-THEN rules. Backpropagation learning with the new activation function also has low computational complexity. Several application examples ( XOR gate, chaotic time-series prediction, channel equalization, and independent component analysis) support the potential of the proposed scheme.