本文選題：生態(tài)效率 + 評(píng)價(jià)與預(yù)測(cè)　； 參考：《華北理工大學(xué)》2017年碩士論文

【摘要】：隨著社會(huì)經(jīng)濟(jì)的發(fā)展,經(jīng)濟(jì)增長(zhǎng)與資源環(huán)境的矛盾日益凸顯,為了協(xié)調(diào)經(jīng)濟(jì)與資源環(huán)境的關(guān)系,開(kāi)展生態(tài)效率的相關(guān)研究具有重大意義。首先利用非線性回歸與信息熵針對(duì)核主成分分析法應(yīng)用中核函數(shù)和核參數(shù)的選取進(jìn)行了探討,基于UCI和統(tǒng)計(jì)年鑒原數(shù)據(jù)集擬合信息熵與核函數(shù)參數(shù)的經(jīng)驗(yàn)公式22?(10)(28)291.11274-410.21300612.9958-xx。其次在歸納綜合了生態(tài)效率指標(biāo)選擇的目標(biāo)及原則的基礎(chǔ)上,針對(duì)目前國(guó)內(nèi)外對(duì)生態(tài)效率的研究以及研究存在的問(wèn)題,結(jié)合當(dāng)前經(jīng)濟(jì)發(fā)展與資源環(huán)境的關(guān)系,對(duì)原有的生態(tài)效率評(píng)價(jià)、預(yù)測(cè)指標(biāo)體系進(jìn)行了改進(jìn)。利用核主成分分析法、數(shù)據(jù)包絡(luò)分析法,對(duì)經(jīng)典的生態(tài)效率評(píng)價(jià)模型進(jìn)行改進(jìn),建立了KPCA-DEA復(fù)合評(píng)價(jià)模型。接著,應(yīng)用核主成分分析法、BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)法等方法對(duì)經(jīng)典預(yù)測(cè)模型進(jìn)行改進(jìn),建立了KPCA-BP復(fù)合預(yù)測(cè)模型。實(shí)證分析結(jié)果顯示,DEA模型評(píng)價(jià)的綜合技術(shù)效率值有14個(gè)為1,PCA-DEA模型的綜合技術(shù)效率值為1的有12個(gè),而KPCA-DEA模型綜合技術(shù)效率值有6個(gè)為1,從綜合技術(shù)效率值結(jié)果表明,KPCA-DEA復(fù)合評(píng)價(jià)模型的效果優(yōu)于PCADEA和DEA。說(shuō)明生態(tài)效率復(fù)合評(píng)價(jià)中具有一定的優(yōu)勢(shì)和可行性。KPCA-BP復(fù)合預(yù)測(cè)模型實(shí)證分析顯示,KPCA-BP的相對(duì)誤差為1.56%,PCA-BP相對(duì)誤差為17.15%,而B(niǎo)P模型預(yù)測(cè)的相對(duì)誤差為20.75%,結(jié)果表明KPCA-BP復(fù)合預(yù)測(cè)模在預(yù)測(cè)中具有一定的優(yōu)勢(shì)。通過(guò)本研究豐富了生態(tài)效率的評(píng)價(jià)和預(yù)測(cè)方法,為生態(tài)效率的評(píng)價(jià)與預(yù)測(cè)提供了參考。
[Abstract]:With the development of social economy, the contradiction between economic growth and resource environment is becoming increasingly prominent. In order to coordinate the relationship between economy and resources and environment, it is of great significance to carry out the research on ecological efficiency. Firstly, using nonlinear regression and information entropy, the selection of kernel function and kernel parameters in the application of kernel principal component analysis is discussed. Based on the empirical formula of fitting information entropy and kernel function parameters based on UCI and statistical yearbook, the empirical formula 22291.11274-410.21300612.9958-xxx. Secondly, on the basis of summing up the objectives and principles of ecological efficiency index selection, aiming at the current domestic and foreign research on ecological efficiency and the existing problems, combined with the current economic development and the relationship between resources and environment, The original ecological efficiency evaluation and prediction index system were improved. Using kernel principal component analysis (KPCA) and data envelopment analysis (DEA), the classical ecological efficiency evaluation model was improved and the KPCA-DEA compound evaluation model was established. Then, the kernel principal component analysis (KPCA) method and BP neural network prediction method are used to improve the classical prediction model, and the KPCA-BP composite prediction model is established. The results of empirical analysis show that 14 of the comprehensive technical efficiency values of DEA model evaluation are 12 of the 1PCA-DEA models with a comprehensive technical efficiency value of 1. The synthetic technical efficiency of KPCA-DEA model is 6. The result shows that the composite evaluation model of KPCA-DEA is better than that of PCADEA and DEA. The empirical analysis of KPCA-BP composite prediction model shows that the relative error of KPCA-BP is 1.56and that of PCA-BP is 17.150.The relative error of BP model is 20.750.The result shows that KPCA-BP has a relative error of 20.75. the result shows that the relative error of KPCA-BP is 17.15, and that of BP model is 20.75. the result shows that the relative error of KPCA-BP is 17.15. The model has some advantages in prediction. This study enriches the evaluation and prediction methods of ecological efficiency and provides a reference for the evaluation and prediction of ecological efficiency.
【學(xué)位授予單位】：華北理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F205;O212.1

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