灰色广义回归神经网络在木薯产量预测中的应用

将GM(1,1)预测模型与广义回归神经网络(GRNN)相融合,构建一种兼具两者优点、互补型的灰色广义回归神经网络(GGRNN).以1985-2007年度广西木薯鲜薯总产量为数据样本,采用GGRNN模型进行广西木薯产量预测研究.研究结果表明,GGRNN训练期平均拟合指数、预测期平均拟合指数分别为0.99和0.93,分别比GM(1,1)模型高0.09和0.04.该组合模型在拟合精度和预测精度方面均优于单一的GM(1,1)预测模型,并具有自学习能力、非线性映射能力以及适应性强等优点,为木薯产量预测的定量化和智能化提供了一条有效途径.

Application of General Regression Neural Network (GGRNN) on Predicting Yield of Cassava

Gray general regression neural network (GGRNN) is constructed by combining gray model GM (1, 1)] and general regression neural network (GRNN) with their advantages and a complementary for each other. In the present study, the use of GGRNN was illustrated by the yield prediction of cassava. The total yield of cassava in Guangxi during 1985-2007 was used as data samples to predict the yield of cassava during 2004-2007 by GGRNN model. The results showed that the average FI in training time and predicting time of GGRNN was 0.99 and 0.93, with an increasing of 0.09 and 0.04 as compared to GM(1,1), respectively. The fitting and predicting precision of GGRRN were better than that of GM(1,1), and GGRRN had advantages on convenience of calculation, nonlinear mapping ability and wide suitability, etc. So it would provide an effective method on quantitative and intelligent prediction of yield of cassava.