Metadaten

Recommended Citation

Debes K, Koenig A, Gross H (2005). Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial. Brains, Minds and Media, Vol. 2005, bmm151. (urn:nbn:de:0009-3-1515)

BibTeX

@Article{Debes:2005,
  author  = "Klaus Debes, Alexander Koenig, Horst-Michael Gross", 
  title   = "Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial",
  journal = "Brains, Minds and Media",
  number  = "1",
  year    = "2005"
}

Full Metadata

Bibliographic Citation Brains, Minds and Media, Volume 2005, Number 1 (2005-07-04)
Title Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial (eng)
Author Klaus Debes, Alexander Koenig, Horst-Michael Gross
Language eng
Abstract Artificial neural networks are based on computational units that resemble basic information processing properties of biological neurons in an abstract and simplified manner. Generally, these formal neurons model an input-output behaviour as it is also often used to characterize biological neurons. The neuron is treated as a black box; spatial extension and temporal dynamics present in biological neurons are most often neglected. Even though artificial neurons are simplified, they can show a variety of input-output relations, depending on the transfer functions they apply. This unit on transfer functions provides an overview of different transfer functions and offers a simulation that visualizes the input-output behaviour of an artificial neuron depending on the specific combination of transfer functions.
Subject ANN, activation function, output function, education, simulation
Classified Subjects ddc: Neuroinformatics (N3985)
DDC 004 570 500
Rights DPPL
URN urn:nbn:de:0009-3-1515
DOI