發(fā)布時(shí)間：2018-01-16 23:33

  本文關(guān)鍵詞：基于IOWHA算子的物流需求組合預(yù)測(cè)模型　出處：《河北大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 組合預(yù)測(cè) 物流需求 相關(guān)性指標(biāo) IOWHA算子

【摘要】：隨著經(jīng)濟(jì)全球化和一體化進(jìn)程的加快，物流業(yè)作為前景廣闊的新興服務(wù)業(yè)已在世界各國(guó)發(fā)展起來(lái)，對(duì)經(jīng)濟(jì)的影響越來(lái)越大，發(fā)展現(xiàn)代物流業(yè)顯然已經(jīng)成為各國(guó)、各區(qū)域經(jīng)濟(jì)快速發(fā)展的必然趨勢(shì)。為了建設(shè)高效運(yùn)作的物流系統(tǒng)，制定出合理、有效的物流發(fā)展政策以適應(yīng)經(jīng)濟(jì)的發(fā)展，，就要進(jìn)行物流需求預(yù)測(cè)。在此背景下，分析社會(huì)經(jīng)濟(jì)活動(dòng)對(duì)物流需求的影響，建立適當(dāng)?shù)亩款A(yù)測(cè)模型，對(duì)物流需求進(jìn)行科學(xué)預(yù)測(cè)，可以為物流規(guī)劃和物流需求態(tài)勢(shì)分析提供重要依據(jù)。論文旨在結(jié)合物流需求預(yù)測(cè)相關(guān)理論和研究現(xiàn)狀，建立物流需求預(yù)測(cè)指標(biāo)體系，選取合適的單項(xiàng)預(yù)測(cè)方法，并在此基礎(chǔ)上構(gòu)建組合預(yù)測(cè)模型，分別對(duì)實(shí)例進(jìn)行預(yù)測(cè)和分析，以尋找能夠提高預(yù)測(cè)精度的方法。 本文首先對(duì)物流需求的基本理論進(jìn)行概述，包括物流需求的定義、特點(diǎn)及主要經(jīng)濟(jì)影響因素，并在考慮物流需求預(yù)測(cè)指標(biāo)選取原則的基礎(chǔ)上，結(jié)合我國(guó)物流歷史統(tǒng)計(jì)數(shù)據(jù)不足的現(xiàn)狀，重點(diǎn)闡述了選取各經(jīng)濟(jì)指標(biāo)、貨運(yùn)量及貨物周轉(zhuǎn)量作為物流需求預(yù)測(cè)所需量化指標(biāo)的合理性，據(jù)此構(gòu)建了物流需求預(yù)測(cè)指標(biāo)體系。然后根據(jù)物流需求預(yù)測(cè)特點(diǎn)和預(yù)測(cè)方法特點(diǎn)的分析，論文選取灰色預(yù)測(cè)法和RBF神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)模型的構(gòu)建�；诖�，針對(duì)單項(xiàng)預(yù)測(cè)方法提供信息有限、預(yù)測(cè)誤差大及傳統(tǒng)組合預(yù)測(cè)方法權(quán)系數(shù)不變、目標(biāo)準(zhǔn)則單一的問(wèn)題，引用最優(yōu)加權(quán)組合建模理論，將灰色關(guān)聯(lián)度、向量夾角余弦和相關(guān)系數(shù)分別與IOWHA算子相結(jié)合，提出3種新的組合預(yù)測(cè)模型權(quán)重確定方法，并應(yīng)用新的權(quán)重確定方法，構(gòu)建了3種基于灰色模型和RBF神經(jīng)網(wǎng)絡(luò)模型的最優(yōu)組合預(yù)測(cè)模型。最后，應(yīng)用灰色預(yù)測(cè)模型、RBF神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型和3種組合預(yù)測(cè)模型分別對(duì)北京市物流需求進(jìn)行預(yù)測(cè)，通過(guò)比較分析說(shuō)明基于相關(guān)性指標(biāo)的IOWHA算子組合預(yù)測(cè)模型能夠有效提高預(yù)測(cè)精度。
[Abstract]:With the acceleration of economic globalization and integration, the logistics industry as a promising emerging service industry has been developed in the world, the impact on the economy is growing, the development of modern logistics industry has obviously become a country. The inevitable trend of the rapid development of regional economy. In order to build an efficient logistics system and formulate reasonable and effective logistics development policies to adapt to the economic development, we must forecast the logistics demand. This paper analyzes the influence of social economic activities on logistics demand, establishes appropriate quantitative forecasting model, and makes scientific prediction of logistics demand. It can provide important basis for logistics planning and logistics demand situation analysis. The purpose of this paper is to establish a logistics demand forecasting index system and select a suitable single forecasting method combined with logistics demand forecasting theory and research status. On this basis, the combined prediction model is constructed, and the examples are forecasted and analyzed to find the method to improve the prediction accuracy. This paper first summarizes the basic theory of logistics demand, including the definition of logistics demand, characteristics and main economic factors, and on the basis of considering the principle of logistics demand prediction index selection. Combined with the current situation of insufficient historical statistical data of logistics in China, the rationality of selecting various economic indicators, freight volume and freight turnover as the quantitative indicators needed for logistics demand prediction is emphasized. According to the analysis of the characteristics of logistics demand forecasting and forecasting methods, the paper selects the grey forecasting method and RBF neural network to build the forecasting model. Aiming at the problems of limited information provided by single prediction method, large prediction error, constant weight coefficient of traditional combination forecasting method and single objective criterion, the grey correlation degree is introduced by using the optimal weighted combination modeling theory. Based on the combination of vector angle cosine and correlation coefficient with IOWHA operator, three new methods for determining the weight of combined prediction model are proposed, and a new weight determination method is applied. Three optimal combination forecasting models based on grey model and RBF neural network model are constructed. Finally, the grey prediction model is applied. RBF neural network forecasting model and three combined forecasting models are used to forecast the logistics demand of Beijing. The comparison and analysis show that the IOWHA operator combination prediction model based on correlation index can effectively improve the prediction accuracy.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP183;F259.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳華友;熵值法及其在確定組合預(yù)測(cè)權(quán)系數(shù)中的應(yīng)用[J];安徽大學(xué)學(xué)報(bào)(自然科學(xué)版);2003年04期

2 李雋波;孫麗娜;;基于多元線性回歸分析的冷鏈物流需求預(yù)測(cè)[J];安徽農(nóng)業(yè)科學(xué);2011年11期

3 于忠霞;李家偉;;組合預(yù)測(cè)法在成都公路物流需求預(yù)測(cè)中的應(yīng)用[J];中國(guó)儲(chǔ)運(yùn);2008年03期

4 唐小我;組合預(yù)測(cè)誤差信息矩陣研究[J];電子科技大學(xué)學(xué)報(bào);1992年04期

5 陳華友,趙佳寶,劉春林;基于灰色關(guān)聯(lián)度的組合預(yù)測(cè)模型的性質(zhì)[J];東南大學(xué)學(xué)報(bào)(自然科學(xué)版);2004年01期

6 王艷芹;張維;;基于MATLAB的RBF神經(jīng)網(wǎng)絡(luò)建模及應(yīng)用[J];大慶師范學(xué)院學(xué)報(bào);2007年02期

7 袁宏俊;胡凌云;劉國(guó)璧;;改進(jìn)的加權(quán)調(diào)和平均算子及其在多屬性決策中應(yīng)用[J];湖南工程學(xué)院學(xué)報(bào)(自然科學(xué)版);2011年02期

8 周禮剛;趙娟;陳華友;丁子千;;基于向量夾角余弦的IOWGA算子組合預(yù)測(cè)模型[J];合肥工業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年09期

9 蔡正高;朱敦贛;;4種誘導(dǎo)有序加權(quán)平均模型的構(gòu)建[J];合肥學(xué)院學(xué)報(bào)(自然科學(xué)版);2008年04期

10 張潛;;物流需求灰色預(yù)測(cè)及其實(shí)證分析[J];哈爾濱工業(yè)大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2010年01期

本文編號(hào)：1435348