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Perceptrons with polynomial post-processing
Griffith Univ, Nathan, Australia.
Griffith Univ, Nathan, Australia.
Griffith Univ, Nathan, Australia.
Griffith Univ, Nathan, Australia.
1996 (English)In: Proceedings of the International Conference on Tools with Artificial Intelligence, 1996, p. 472-474Conference paper, Published paper (Refereed)
Abstract [en]

Introduces 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 problem by 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 'generalised' product networks which, however, require a complex approximation theory of the Müntz-Szasz-Ehrenpreis type. A backpropagation 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.

Place, publisher, year, edition, pages
1996. p. 472-474
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:mdh:diva-2979DOI: 10.1109/TAI.1996.560792ISBN: 0-8186-7686-7 (print)OAI: oai:DiVA.org:mdh-2979DiVA, id: diva2:115643
Conference
1996 IEEE 8th International Conference on Tools with Artificial Intelligence; Toulouse, Fr; ; 16 November 1996 through 19 November 1996
Available from: 2008-03-08 Created: 2008-03-08 Last updated: 2015-07-07Bibliographically approved

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