| Literature DB >> 18244604 |
E Soria-Olivas1, J D Martin-Guerrero, G Camps-Valls, A J Serrano-Lopez, J Calpe-Maravilla, L Gomez-Chova.
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.Entities:
Year: 2003 PMID： 18244604 DOI： 10.1109/TNN.2003.820444
Source DB: PubMed Journal: IEEE Trans Neural Netw ISSN： 1045-9227