科技通报
科技通報
과기통보
BULLETIN OF SCIENCE AND TECHNOLOGY
2015年
1期
195-198,209
,共5页
指数平滑%多元线性回归%灰色系统%SOR-LS-SVM%公路旅游客流量
指數平滑%多元線性迴歸%灰色繫統%SOR-LS-SVM%公路旅遊客流量
지수평활%다원선성회귀%회색계통%SOR-LS-SVM%공로여유객류량
exponential smoothing%multiple linear regression%grey system%successive over-relaxation for least squares support vector machine%highway traveling passenger volume
基于指数平滑、多元线性回归、灰色系统等目前常见的预测方法建立超松弛改进的最小二乘支持向量机算法的公路旅游客流量组合预测模型。通过实例验证和比较，展示了基于超松弛最小二乘支持向量机算法的公路交通旅游客流量组合预测模型具有较好的预测效果和较高的应用价值。
基于指數平滑、多元線性迴歸、灰色繫統等目前常見的預測方法建立超鬆弛改進的最小二乘支持嚮量機算法的公路旅遊客流量組閤預測模型。通過實例驗證和比較，展示瞭基于超鬆弛最小二乘支持嚮量機算法的公路交通旅遊客流量組閤預測模型具有較好的預測效果和較高的應用價值。
기우지수평활、다원선성회귀、회색계통등목전상견적예측방법건립초송이개진적최소이승지지향량궤산법적공로여유객류량조합예측모형。통과실례험증화비교，전시료기우초송이최소이승지지향량궤산법적공로교통여유객류량조합예측모형구유교호적예측효과화교고적응용개치。
By these current common forecast methods of exponential smoothing, multiple linear regression and grey system, the combined forecast model is established on Least Squares Support Vector Machine improved by successive over-relaxation. Through example confirmation and comparison, it is shown that the combined forecast model of Highway traveling passenger volume based on Least Squares Support Vector Machine improved by successive over-relaxation has good forecast effect and high application value.
