A low-complexity fuzzy activation function for artificial neural networks

Author

Soria-Olivas, E. ; Martin-Guerrero, J.D. ; Camps-Valls, G. ; Serrano-Lopez, A.J. ; Calpe-Maravilla, J. ; Gomez-Chova, L.

Author_Institution

Dept. of Enginyeria Electronica, Univ. de Valencia, Spain

Volume

14

Issue

6

fYear

2003

Firstpage

1576

Lastpage

1579

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.

Keywords

backpropagation; computational complexity; fuzzy logic; multilayer perceptrons; transfer functions; artificial neural network; backpropagation learning; computational complexity; fuzzy logic; hardware implementation; if-then rules; low-complexity fuzzy activation function; rule extraction; Artificial neural networks; Backpropagation; Chaos; Computational complexity; Fuzzy logic; Fuzzy neural networks; Hardware; Independent component analysis; Neural networks; Neurons;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2003.820444

Filename

1257422