發(fā)布時(shí)間：2018-02-28 06:27

  本文關(guān)鍵詞： 紡織業(yè) 成本預(yù)警 數(shù)據(jù)挖掘 BP神經(jīng)網(wǎng)絡(luò) 支持向量機(jī) AC算法　出處：《福州大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代工業(yè)飛速發(fā)展,市場(chǎng)競(jìng)爭(zhēng)日益激烈,成本已成為影響行業(yè)發(fā)展的重要因素,所以成本的管理就變得非常關(guān)鍵的。紡織業(yè)作為我國(guó)國(guó)民經(jīng)濟(jì)傳統(tǒng)支柱產(chǎn)業(yè),為社會(huì)發(fā)展做出了很大的貢獻(xiàn)。2012年,為了貫徹落實(shí)《國(guó)民經(jīng)濟(jì)和社會(huì)發(fā)展第十二個(gè)五年規(guī)劃綱要》和《工業(yè)轉(zhuǎn)型升級(jí)規(guī)劃(2011-2015年)》,工業(yè)和信息化部制定并發(fā)布了《紡織工業(yè)“十二五”發(fā)展規(guī)劃》,指出了紡織行業(yè)在國(guó)際和國(guó)內(nèi)上都面臨巨大的挑戰(zhàn),匯率的變動(dòng)、國(guó)際間貿(mào)易壁壘、行業(yè)原材料價(jià)格上漲、勞動(dòng)成本增加、技術(shù)改革、環(huán)境、能源等方面的因素都很大地影響紡織行業(yè)的發(fā)展,而且各因素都間接或直接地影響著行業(yè)的成本發(fā)生,對(duì)于一個(gè)利潤(rùn)空間本來就小的紡織行業(yè),成本的管理就變得尤為重要。因此,研究紡織業(yè)的成本,建立行業(yè)的成本預(yù)警系統(tǒng)是有必要的。凡事預(yù)則立,不預(yù)則廢,建立有效的成本預(yù)警系統(tǒng)可以幫助行業(yè)進(jìn)行成本分析,控制成本風(fēng)險(xiǎn),提高行業(yè)經(jīng)濟(jì)效益,進(jìn)而促進(jìn)紡織業(yè)的可持續(xù)發(fā)展。所以,本文立足于紡織業(yè),進(jìn)行成本預(yù)警研究。首先,闡述了進(jìn)行紡織業(yè)成本預(yù)警研究的背景及意義,總結(jié)國(guó)內(nèi)外相關(guān)研究現(xiàn)狀；并介紹了成本預(yù)警相關(guān)理論、數(shù)據(jù)挖掘的定義、BP神經(jīng)網(wǎng)絡(luò)算法、支持向量機(jī)算法和相似體合成算法。其次,根據(jù)成本預(yù)警相關(guān)文獻(xiàn)研究和紡織業(yè)現(xiàn)狀,從定性的角度分析影響成本發(fā)生的因素,再結(jié)合灰關(guān)聯(lián)法的定量分析,確立紡織業(yè)成本預(yù)警指標(biāo)體系；劃分警情指標(biāo)的警度值,量化預(yù)警區(qū)間；并利用逐步回歸法進(jìn)行指標(biāo)約簡(jiǎn),消除不顯著變量,減少模型的輸入維數(shù)；然后基于數(shù)據(jù)挖掘的方法構(gòu)建BP-SVM-AC預(yù)警模型,分兩個(gè)階段進(jìn)行成本預(yù)警研究,先利用一階段改進(jìn)的BP-SVM模型確定成本警情的警度和強(qiáng)度,再借助AC算法,進(jìn)行二階段的成本警情預(yù)測(cè)。最后,借助數(shù)值計(jì)算軟件Matlab R2012a進(jìn)行實(shí)證研究。通過對(duì)BP-SVM-AC預(yù)警模型的結(jié)果分析,證明了該模型具有較好的預(yù)警效果。所以本文建立的BP-SVM-AC預(yù)警模型具有一定的科學(xué)性和實(shí)用性,并且通過該模型可以預(yù)測(cè)紡織業(yè)未來成本警情,幫助該行業(yè)的相關(guān)管理部門提前掌握成本警情的發(fā)展趨勢(shì),有利于他們及時(shí)采取措施應(yīng)對(duì)警情發(fā)生,進(jìn)而也為行業(yè)企業(yè)進(jìn)行成本預(yù)警提供一定的參考價(jià)值。
[Abstract]:With the rapid development of modern industry and the increasingly fierce market competition, cost has become an important factor affecting the development of the industry, so cost management has become very critical. Made a great contribution to social development. In 2012, In order to implement the outline of the Twelfth Five-Year Plan for National Economic and Social Development and the Plan for Industrial Transformation and upgrading 2011-2015, the Ministry of Industry and Informatization formulated and issued the 12th Five-Year Plan for Textile Industry, pointing out that. The textile industry is facing enormous challenges both internationally and domestically. Changes in exchange rates, international trade barriers, rising prices of raw materials in the industry, increased labour costs, technological reforms, environment, energy and other factors have greatly affected the development of the textile industry. And all factors have an indirect or direct impact on the cost of the industry, and for a textile industry where profit margins are small, cost management becomes particularly important. It is necessary to set up an industry cost warning system. It is necessary to establish an effective cost warning system that can help the industry to carry out cost analysis, control the cost risk, and improve the economic benefits of the industry. Therefore, this paper bases on the textile industry, carries on the cost early warning research. First, elaborated the textile industry cost early warning research background and the significance, summarized the domestic and foreign correlation research present situation; The related theory of cost early warning, the definition of data mining, the BP neural network algorithm, the support vector machine algorithm and the similar volume synthesis algorithm are introduced. Based on the qualitative analysis of the factors affecting the occurrence of cost, combining with the quantitative analysis of grey correlation method, the paper establishes the cost early warning index system of textile industry, classifies the alarm value of the warning index, and quantifies the early warning interval. Using stepwise regression method to reduce the index, eliminate the unsignificant variables, reduce the input dimension of the model, then based on the method of data mining to construct the BP-SVM-AC early warning model, and carry out the cost early warning research in two stages. First, the one-stage improved BP-SVM model is used to determine the alarm degree and intensity of the cost alarm, and then the two-stage cost alarm prediction is carried out with the help of AC algorithm. Finally, Through the analysis of the results of the BP-SVM-AC early warning model, it is proved that the model has good early warning effect. Therefore, the BP-SVM-AC early-warning model established in this paper is scientific and practical. And through this model, we can predict the future cost warning of textile industry, help the relevant management department of this industry to grasp the development trend of cost warning in advance, and help them to take measures to deal with the situation in time. Furthermore, it also provides certain reference value for industry enterprises to carry out cost warning.
【學(xué)位授予單位】：福州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F426.81;F406.7;TP311.13

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