基于粗糙集的粮食产量SVM非线性组合预测模型

In this paper，aiming at it's own prediction of grain output，draw into support vector machine intelligent optimization algorithms and combination prediction technique. Employ the method of determining weight coefficient based on rough set theory. Establish a combination forecasting method on the basis of standard rough set theory. Use the SOM method of self-organizing neural network to discretizationize attribute property in order to establish information systems and decision table. Transform determining weight coefficient into the evaluation of attribute significance among standard rough set theory，work out weight coefficient of single model amid combination prediction model. Use constructed combination prediction model，predict the historical data of grain gross output in Heilongjiang reclamation areas. It shows the high accuracy of constructed combination prediction model in predicting the grain output. Be consistent with the true value. Mean absolute error of the prediction results get lower than traditional modeling method.