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Feedforward Neural Network Estimation of a Crop Yield Response Function

Published online by Cambridge University Press:  28 April 2015

Wayne H. Joerding ,
Ying Li  and
Douglas L. Young
Wayne H. Joerding
Affiliation:
Department of Economics, Washington State University, Pullman, WA
Ying Li
Affiliation:
FannieMae, Washington DC
Douglas L. Young
Affiliation:
Department of Agricultural Economics, Washington State University, Pullman, WA
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Abstract

Feedforward networks have powerful approximation capabilities without the “explosion of parameters” problem faced by Fourier and polynomial expansions. This paper first introduces feedforward networks and describes their approximation capabilities, then we address several practical issues faced by applications of feedforward networks. First, we demonstrate networks can provide a reasonable estimate of a Bermudagrass hay fertilizer response function with the relatively sparse data often available from experiments. Second, we demonstrate that the estimated network with a practical number of hidden units provides reasonable flexibility. Third, we show how one can constrain feedforward networks to satisfy a priori information without losing their flexible functional form characteristic.

Keywords

neural networksbiological process modelsfeedforward networksproduction function
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 26 , Issue 1 , July 1994 , pp. 252 - 263
Copyright
Copyright © Southern Agricultural Economics Association 1994

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