铁道科学与工程学报2017，Vol.14Issue(11)：2345-2351,7.
优化GM(1,1)与SVM组合模型的路基冻胀预测应用
The forecast application of subgrade's frost-heaving index based on optimized GM(1,1) and SVM combination model
摘要
Abstract
The paper proposed a forecasting algorithm combining the optimized non-equal interval GM(1,1) model and the adopted support vector machine to rectify the initial forecast of residual errors. This algorithm was then used to forecast the railway frost-heaving data quantitatively. It optimized the calculation methods of traditional non-equal time interval GM(1,1) forecast model's differential equation's background value and model's initial value. The time interval weight matrix was set, and different weights on the data measured at different times were assigned. The adopted support vector machine was applied to rectify the initial forecast of residual errors to get the final forecast. The model in this paper was applied in matching and forecasting the frost-heaving data of subgrades that are collected from Harbin-Dalian Passenger Railway. The model's posterior error ratio is only 0.005. The average prediction error value is 2.039% and the maximum prediction error value is 5.911%. It is more accurate than the current GM(1,1) models and the combined forecasting algorithm proposed in the literature. Thus, the highly accurate quantitative forecast of the frost-heaving was achieved.关键词
铁道工程/路基冻胀/支持向量机/灰色模型/组合预测/残差修正Key words
railway engineering/railroad subgrade's frost-heaving index/support vector machine/grey model/combination forecasting algorithm/residual errors revise分类
交通工程引用本文复制引用
乐天晗,吴永军,吴湘华,陈峰..优化GM(1,1)与SVM组合模型的路基冻胀预测应用[J].铁道科学与工程学报,2017,14(11):2345-2351,7.基金项目
铁道部重点资助项目(2012 G009-B) （2012 G009-B）
中国铁路总公司科技研究开发计划资助项目(2014G001-E) （2014G001-E）
