Abstract
A learning algorithm is presented that uses internal representations, which are continuous random variables, for the training of multilayer networks whose neurons have Heaviside characteristics. This algorithm is an improvement in that it is applicable to networks with any number of layers of variable weights and does not require 'bit flipping' on the internal representations to reduce output error. The algorithm is extended to apply to recurrent networks. Some illustrative results are given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Heaviside step functionComputer scienceVariable (mathematics)Layer (electronics)Artificial intelligenceAlgorithmRandom variableMathematicsStatistics
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Publication Info
- Year
- 2002
- Type
- article
- Volume
- 2
- Pages
- 1812-1817
- Citations
- 1
- Access
- Closed
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Cite This
R.J. Gaynier,
T. Downs
(2002).
A method of training multi-layer networks with heaviside characteristics using internal representations.
IEEE International Conference on Neural Networks
, 2
, 1812-1817.
https://doi.org/10.1109/icnn.1993.298832
Identifiers
- DOI
- 10.1109/icnn.1993.298832