一种基于灰色系统和支持向量机的预测优化模型

针对传统的灰色系统中预测模型涉及相关因素多,预测效率与精度不足等问题,结合粗糙集理论和支持向量回归机方法,提出了一种改进的预测优化算法。该模型算法首先利用属性约简技术解决影响因子不相容性问题并有效缩减了影响预测值的因子空间,降低计算的复杂性;然后采用灰色模型进行数据预测,并将预测结果作为支持向量机的输入,进而求解优化模型的预测值,最后采用1990～2010年我国人口数据对我国人口进行预测。实验结果表明该预测优化模型在预测效率和精度方面具有较好的表现。

An optimization model for forecasting based on grey system and support vector machine

Prediction models in traditional gray system involved various factors and fell short in predicting efficiency and precision. An optimized prediction model was put forward by combining the rough theory and the SVM method. The attribute deduction method was first employed on the inconsistent decision table to seek for the core attribute set, which could enable the prediction model to focus better on narrow and specific attribute fields with higher efficiency. A gray model was applied in the optimized dataset. The result parameters were then treated as the input data of a support vector machine for model prediction. China’s census data (1990～2010) were also applied in population prediction. Experimental results showed that this model had better accuracy and higher efficiency than the existing models.