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