Application ofGeneralized Regression Neural Network to the Agricultural Machinery Demand Forecasting

被引：9
|
作者：
Luo, Wei [1 ]
Fu, Zhuo [1 ]
机构：
[1] Cent S Univ, Changsha 410075, Hunan, Peoples R China
来源：
ADVANCES IN MECHATRONICS AND CONTROL ENGINEERING, PTS 1-3 | 2013年 / 278-280卷
关键词：
generalized regression neural network; agriculture machinery; demand forecasting; cross validation;
D O I：
10.4028/www.scientific.net/AMM.278-280.2177
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：2177 / +
页数：2
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