交通运输系统工程与信息
交通運輸繫統工程與信息
교통운수계통공정여신식
JOURNAL OF COMMUNICATION AND TRANSPORTATION SYSTEMS ENGINEERING AND INFORMATION
2015年
1期
137-142,158
,共7页
物流工程%预测方法%最小二乘支持向量机%物流需求%预测精度
物流工程%預測方法%最小二乘支持嚮量機%物流需求%預測精度
물류공정%예측방법%최소이승지지향량궤%물류수구%예측정도
logistics engineering%forecasting method%LSSVM%logistics demand%forecasting accuracy
为降低物流需求建模中最小二乘支持向量机（LSSVM）的结构复杂性、进一步提高LSSVM对物流需求的预测精度，提出一种基于灰色关联分析（GRA）与核主成分分析（KPCA）的LSSVM预测方法。首先利用GRA找出物流需求的主要影响因素；然后利用KPCA提取主要影响因素的非线性主成分，消除因素之间的多重相关性；最后，将提取出的非线性主成分作为LSSVM的输入变量，构建物流需求预测模型，并采用改进粒子群(IPSO)算法调整LSSVM参数。运用该方法对我国物流需求进行实例分析，结果表明，该方法有效减少了LSSVM输入变量个数，简化了LSSVM结构，并且在一定程度上提高了物流需求预测精度。
為降低物流需求建模中最小二乘支持嚮量機（LSSVM）的結構複雜性、進一步提高LSSVM對物流需求的預測精度，提齣一種基于灰色關聯分析（GRA）與覈主成分分析（KPCA）的LSSVM預測方法。首先利用GRA找齣物流需求的主要影響因素；然後利用KPCA提取主要影響因素的非線性主成分，消除因素之間的多重相關性；最後，將提取齣的非線性主成分作為LSSVM的輸入變量，構建物流需求預測模型，併採用改進粒子群(IPSO)算法調整LSSVM參數。運用該方法對我國物流需求進行實例分析，結果錶明，該方法有效減少瞭LSSVM輸入變量箇數，簡化瞭LSSVM結構，併且在一定程度上提高瞭物流需求預測精度。
위강저물류수구건모중최소이승지지향량궤（LSSVM）적결구복잡성、진일보제고LSSVM대물류수구적예측정도，제출일충기우회색관련분석（GRA）여핵주성분분석（KPCA）적LSSVM예측방법。수선이용GRA조출물류수구적주요영향인소；연후이용KPCA제취주요영향인소적비선성주성분，소제인소지간적다중상관성；최후，장제취출적비선성주성분작위LSSVM적수입변량，구건물류수구예측모형，병채용개진입자군(IPSO)산법조정LSSVM삼수。운용해방법대아국물류수구진행실례분석，결과표명，해방법유효감소료LSSVM수입변량개수，간화료LSSVM결구，병차재일정정도상제고료물류수구예측정도。
To reduce the complex structure of least squares support vector machine (LSSVM) in logistics demand modeling and improve the forecasting accuracy of LSSVM for logistics demand further, based on the grey relational analysis (GRA) and the kernel principal component analysis (KPCA), a LSSVM forecasting method is proposed. First, GRA is used to choose the main influential factors of logistics demand. Then, the KPCA is applied to extract the nonlinear principal components, which can eliminate the correlation in the main influential factors. Finally, the extracted nonlinear principal components are selected as the input variables of LSSVM to construct the logistics demand forecasting model. And the parameters of LSSVM are adjusted by the improved particle swarm optimization (IPSO). Using this method, China’s logistics demand is analyzed. The results indicate that the proposed method effectively reduces the number of the input variables in LSSVM and simplifies the structure of the LSSVM. The forecasting accuracy of logistics demand is improved to some degree.
