Title of article
A low-complexity fuzzy activation function for artificial neural networks
Author/Authors
E.، Soria-Olivas, نويسنده , , J.D.، Martin-Guerrero, نويسنده , , G.، Camps-Valls, نويسنده , , A.J.، Serrano-Lopez, نويسنده , , J.، Calpe-Maravilla, نويسنده , , L.، Gomez-Chova, نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2003
Pages
-1575
From page
1576
To page
0
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.
Journal title
IEEE TRANSACTIONS ON NEURAL NETWORKS
Serial Year
2003
Journal title
IEEE TRANSACTIONS ON NEURAL NETWORKS
Record number
62783
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