主成分和BP神经网络在粮食产量预测中的组合应用

Application of PCA and BP Neural Networks in Grain Production Prediction

粮食产量的变动受到多种因素的共同影响,各因素之间往往具有十分复杂的非线性关系,传统的预测方法大多无法反映这种变化规律而影响了预测的准确性.BP神经网络模型具有很好的非线性逼近能力,对中国粮食产量能实现比较准确的预测;主成分分析可以对具有模糊关联的变量数据进行降维,其与BP神经网络的组合能优化模型的网络结构,提高预测精度.实证结果表明,组合模型预测结果的精度提高了3%,网络训练的收敛速度和效率也得到不同程度的改善.

The grain output fluctuation is a result of several factors. And there is a very complex nonlinear relation between these factors. Lacking the ability to reflect the nonlinear regulation, most of traditional prediction method leads to low accuracy of prediction. BP neural network model has good nonlinear approximation capacity and it does well in prediction of Chinese grain output. Principal component analysis can be associated with the fuzzy variable data for dimension reduction. The combination of PCA and BPNN can optimize the network structure and improve the prediction precision. The results show that the accuracy of combined model is improved by 3% and the efficiency of network training performance also has been improved in different degree.