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A low-complexity fuzzy activation function for artificial neural networks

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6 Author(s)
Soria-Olivas, E. ; Dept. of Enginyeria Electronica, Univ. de Valencia, Spain ; Martin-Guerrero, J.D. ; Camps-Valls, G. ; Serrano-Lopez, A.J.
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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.

Published in:

Neural Networks, IEEE Transactions on  (Volume:14 ,  Issue: 6 )

Date of Publication:

Nov. 2003

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