Application of Generalized Regression Neural Network to the Agricultural Machinery Demand Forecasting

Wei Luo; Zhuo Fu

doi:10.4028/www.scientific.net/AMM.278-280.2177

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 278-280Application of Generalized Regression Neural...

Application of Generalized Regression Neural Network to the Agricultural Machinery Demand Forecasting

Abstract:

Proposed an agricultural machinery demand forecasting model based on the generalized regression neural network. This model is based on GRNN, using the circulation testing algorithm combined with k-fold cross validation for parameters optimization and network training, and achieves satisfying forecasting precision in the case of small samples. By using the data of total power agricultural machinery and relevant factors from the year 1995 to 2010 in Guangxi province, we tested and verified the effectiveness of the model.

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Periodical:

Applied Mechanics and Materials (Volumes 278-280)

Pages:

2177-2182

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.278-280.2177

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Online since:

January 2013

Authors:

Wei Luo, Zhuo Fu

Keywords:

Agriculture Machinery, Cross Validation, Demand Forecasting, Generalized Regression Neural Network

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