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A low-complexity fuzzy activation function for artificial neural networks

2003; Institute of Electrical and Electronics Engineers; Volume: 14; Issue: 6 Linguagem: Inglês

10.1109/tnn.2003.820444

1941-0093

Emilio Soria‐Olivas, José D. Martín‐Guerrero, Gustau Camps‐Valls, Antonio J. Serrano-López, Javier Calpe‐Maravilla, Luis Gómez‐Chova,

Chaos control and synchronization

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.