基于农业生产条件的河南粮食产量组合模型研究

通过对1990～2007年河南粮食产量的分析，在影响粮食产量的诸多因素中选出农业生产条件等8个主要影响因素。基于粮食生产系统的复杂性，建立偏最小二乘回归与BP神经网络耦合模型。偏最小二乘法通过对自变量中的信息进行组合和提取，有效克服变量之间的多重相关性问题，实现了对高维数据的降维处理，同时降低了神经网络的输入维数，提高了网络的学习效率和稳健性，从而充分利用了2类现代建模方法的优点。结果表明，偏最小二乘神经网络耦合模型研究河南粮食产量的拟合精度和预测精度都比较理想。

Model of Grain Yield in Henan Province Based on Conditions of Agriculture

The grain yield of Henan Province was analyzed from 1990 to 2007.There were many complex factors affecting the grain yield.So a composition model was proposed by combining neural network model with the partial least square method.The factors were analyzed by means of partial least square method to find the most important components,so that the problem of multi-correlation among variables could be solved,and the amount of input dimensions of the neural network could be reduced.When the neural network was app...