基于粗糙集与RBF神经网络的农业总产值预测方法

Forecasting Method for Farming Gross Output Based on Rough Set and RBF Neural Network

提出了把粗糙集和R BF神经网络相结合应用于农业总产值预测的方法。首先用粗糙集对影响农业总产值的多个因素进行属性约简,选择主要影响因素,去除冗余信息;然后利用RBF神经网络建立预测模型。最后对该模型的预测结果与因子分析神经网络模型的预测结果进行了比较,表明了该模型的有效性和优越性。

A new method for forecasting the farming gross output with rough set and RBF neural network has been presented in this paper. Firstly the main influencing factors are chosen based on attributes reduction of rough sets, and the redundancy factors are removed. Then a forecasting model is founded by using RBF neural network. Finally comparisons are made between the forecasting results of the new model and the factor analysis neural network, to prove the efficiency and advantages of the new one.