A low-complexity fuzzy activation function for artificial neural networks.
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.
Soria-Olivas
E
E
Dept. of Enginyeria Electronica, Univ. de Valencia, Spain.
Martin-Guerrero
J D
JD
Camps-Valls
G
G
Serrano-Lopez
A J
AJ
Calpe-Maravilla
J
J
Gomez-Chova
L
L