A Low-Complexity Fuzzy Activation Function for Artificial Neural Networks  

Oleh:

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

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 6 (Nov. 2003), page 1576-1579.
Topik: artificial neural; low - complexity; fuzzy; activation function; artificial; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.9 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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