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approximators

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Guidelines for the selection of network architecture
William C. Carpenter, Margery E. Hoffman
Journal:

AI EDAM / Volume 11 / Issue 5 / November 1997

Published online by Cambridge University Press:

27 February 2009, pp. 395-408
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This paper is concerned with presenting guidelines to aide in the selection of the appropriate network architecture for back-propagation neural networks used as approximators. In particular, its goal is to indicate under what circumstances neural networks should have two hidden layers and under what circumstances they should have one hidden layer. Networks with one and with two hidden layers were used to approximate numerous test functions. Guidelines were developed from the results of these investigations.