本文選題：工程造價(jià) + 宏觀經(jīng)濟(jì)�。� 參考：《天津大學(xué)》2014年碩士論文

【摘要】：建設(shè)工程的造價(jià)是項(xiàng)目管理的一個(gè)重要方面,是投資者、業(yè)主、承包商、分包商以及其它利益相關(guān)者的內(nèi)在驅(qū)動(dòng)因素。我國(guó)雖然處于市場(chǎng)經(jīng)濟(jì)階段,但是定額計(jì)價(jià)模式的觀念仍然存在,如何在競(jìng)爭(zhēng)激烈的國(guó)際舞臺(tái)上立于不敗之地,合理地預(yù)測(cè)建筑的造價(jià)顯得至關(guān)重要。價(jià)格的預(yù)測(cè)僅從構(gòu)成造價(jià)的人、材、機(jī)的變動(dòng)來(lái)分析工程造價(jià)的變動(dòng)已不能滿足動(dòng)態(tài)市場(chǎng)的要求,需要從宏觀經(jīng)濟(jì)的角度來(lái)分析對(duì)工程造價(jià)產(chǎn)生影響的因素。只有充分把握宏觀市場(chǎng)價(jià)格變動(dòng)引起的工程造價(jià)的變動(dòng),合理預(yù)見(jiàn)風(fēng)險(xiǎn),才能在競(jìng)標(biāo)過(guò)程中得心應(yīng)手。本文首先從理論角度分析了工程造價(jià)與宏觀經(jīng)濟(jì)的相互關(guān)系,接著從六個(gè)方面定性地分析了影響工程造價(jià)的宏觀經(jīng)濟(jì)變量:經(jīng)濟(jì)整體水平、物價(jià)變動(dòng)水平、各行業(yè)發(fā)展水平、投資水平、生產(chǎn)率水平、建筑業(yè)競(jìng)爭(zhēng)水平。在此基礎(chǔ)上對(duì)小波分析、神經(jīng)網(wǎng)絡(luò)(ANN)及小波神經(jīng)網(wǎng)絡(luò)(WNN)進(jìn)行了闡述并分析:得出小波神經(jīng)網(wǎng)絡(luò)不僅具有有效運(yùn)用小波變換局部化性質(zhì)的特點(diǎn),還有神經(jīng)網(wǎng)絡(luò)自學(xué)習(xí)能力的特點(diǎn),因而具備良好的逼近、容錯(cuò)能力以及收斂性能好、預(yù)測(cè)準(zhǔn)確的特性�；诖藦木W(wǎng)絡(luò)結(jié)構(gòu)的確定、參數(shù)設(shè)置、算法等方面考慮建立了小波神經(jīng)網(wǎng)絡(luò)模型來(lái)研究宏觀經(jīng)濟(jì)變量對(duì)工程造價(jià)的影響。利用輸入層個(gè)數(shù)為1的小波神經(jīng)網(wǎng)絡(luò)模型確定影響工程造價(jià)的先導(dǎo)變量有國(guó)民生產(chǎn)總值、房屋施工面積、居民消費(fèi)價(jià)格指數(shù)、建筑安裝工程固定資產(chǎn)投資價(jià)格指數(shù)、按建筑業(yè)總產(chǎn)值計(jì)算的建筑企業(yè)勞動(dòng)生產(chǎn)率、人均地區(qū)生產(chǎn)總值。將這些先導(dǎo)變量作為預(yù)測(cè)模型的輸入變量,工程造價(jià)作為輸出變量,驗(yàn)證了小波神經(jīng)網(wǎng)絡(luò)模型在工程造價(jià)預(yù)測(cè)方面有著較強(qiáng)的適用性。
[Abstract]:The cost of construction project is an important aspect of project management, which is the internal driving factor of investors, owners, contractors, subcontractors and other stakeholders. Although our country is in the stage of market economy, but the concept of quota pricing mode still exists, how to be invincible in the competitive international stage, it is very important to reasonably predict the construction cost. The forecast of price can not meet the requirements of the dynamic market only by analyzing the change of the construction cost from the change of the people, materials and machines that constitute the cost, and needs to analyze the influence factors on the project cost from the angle of macro economy. Only by fully grasping the change of engineering cost caused by the change of macro market price and reasonably anticipating the risk can we be able to handle the bidding process. This paper first analyzes the relationship between engineering cost and macro-economy from a theoretical point of view, and then qualitatively analyzes the macroeconomic variables that affect construction cost from six aspects: the overall level of economy, the level of price change, and the level of development of various industries. Investment level, productivity level, construction industry competition level. On this basis, wavelet analysis, neural network ANN) and wavelet neural network (WNN) are expounded and analyzed. It is concluded that wavelet neural network not only has the characteristic of using wavelet transform localization effectively, but also has the characteristics of neural network self-learning ability. Therefore, it has good approximation, fault-tolerant ability, good convergence performance and accurate prediction characteristics. Based on this, a wavelet neural network model is established to study the influence of macroeconomic variables on project cost from the aspects of network structure determination, parameter setting, algorithm and so on. Using the wavelet neural network model with input layer 1 to determine the leading variables that affect the project cost are gross national product (GNP), building construction area, consumer price index, fixed assets investment price index of construction and installation project. Construction enterprise labor productivity, per capita regional GDP calculated by the gross output value of the construction industry. These leading variables are used as input variables and project cost as output variables. It is verified that the wavelet neural network model has strong applicability in engineering cost prediction.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F124;TP183;TU723.3

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相關(guān)期刊論文 前10條

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本文編號(hào)：1955592