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Perceptrons with polynomial post-processing
Mälardalen University, Department of Mathematics and Physics. Griffith University, Australia.
Griffith University, Australia.
Mälardalen University, Department of Mathematics and Physics. Griffith University, Australia.
2000 (English)In: Journal of experimental and theoretical artificial intelligence (Print), ISSN 0952-813X, E-ISSN 1362-3079, Vol. 12, 57-68 p.Article in journal (Refereed) Published
Abstract [en]

We introduce tensor product neural networks, composed of a layer of univariate neurons followed by a net of polynomial post-processing. We look at the general approximation properties of these networks observing in particular their relationship to the Stone-Weierstrass theorem for uniform function algebras. The implementation of the post-processing as a two-layer network, with logarithmic and exponential neurons leads to potentially important `generalized ’ product networks, which however require a complex approximation theory of Mu$ntz-Szasz-Ehrenpreis type. A back-propagation algorithm for product networks is presented and used in three computational experiments. In particular, approximation by a sigmoid product network is compared to that of a single layer radial basis network, and a multiple layer sigmoid network. An additional experiment is conducted, based on an operational system, to further demonstrate the versatility of the architecture.

Place, publisher, year, edition, pages
2000. Vol. 12, 57-68 p.
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:mdh:diva-28590OAI: oai:DiVA.org:mdh-28590DiVA: diva2:840246
Available from: 2015-07-07 Created: 2015-07-07 Last updated: 2015-07-07Bibliographically approved

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