【摘要】：20世紀(jì)以來,國家經(jīng)濟(jì)高速發(fā)展,但發(fā)展的同時(shí)很多社會(huì)問題逐漸暴露出來,如:經(jīng)濟(jì)發(fā)展了,卻帶來許多產(chǎn)品生產(chǎn)過剩的問題;人口平均壽命提高了,卻又伴隨著出現(xiàn)老齡人口的社會(huì)保障問題;經(jīng)濟(jì)發(fā)展的速度加快了,競(jìng)爭(zhēng)激烈了,社會(huì)分配的不公平問題也出現(xiàn)了;工業(yè)發(fā)展的同時(shí),隨著工業(yè)工程中產(chǎn)生的廢水、廢氣的排放,環(huán)境問題就接踵而來了…等等。這些問題說明了經(jīng)濟(jì)的發(fā)展不能代表一個(gè)國家或地區(qū)發(fā)展的綜合水平,是否真正的健康的發(fā)展。社會(huì)發(fā)展水平的說法就由此被提出,眾多學(xué)者著手開始這方面的研究。如何定義社會(huì)發(fā)展的內(nèi)涵,采用什么評(píng)價(jià)方法去評(píng)價(jià)一個(gè)國家或地區(qū)的社會(huì)發(fā)展水平,成為了當(dāng)前理論和實(shí)踐界十分關(guān)注的問題。關(guān)于社會(huì)發(fā)展水平的綜合評(píng)價(jià),目前還沒有國際公認(rèn)的體系和方法,針對(duì)社會(huì)發(fā)展的豐富含義,統(tǒng)計(jì)手段總是有限的,現(xiàn)實(shí)中不存在絕對(duì)完美的測(cè)度和評(píng)價(jià)方法,我們只能根據(jù)研究目的對(duì)社會(huì)發(fā)展指標(biāo)和評(píng)價(jià)方法的研究進(jìn)行再思考、再認(rèn)識(shí),正確地把握社會(huì)發(fā)展的科學(xué)內(nèi)涵和原則,才能進(jìn)一步探討與其相適應(yīng)的科學(xué)的統(tǒng)計(jì)方法,才能盡可能客觀地描述社會(huì)發(fā)展的狀況。本文運(yùn)用理論研究與實(shí)證研究相結(jié)合的方式評(píng)價(jià)貴州省社會(huì)發(fā)展水平。從理論上分析社會(huì)發(fā)展的內(nèi)涵、指標(biāo)選取及篩選方法、綜合評(píng)價(jià)方法,最后建立了四層次指標(biāo)體系模型。文中基于傳統(tǒng)的統(tǒng)計(jì)方法的局限性,站在神經(jīng)網(wǎng)絡(luò)前沿的方向,選取了具有非線性擬合功能的GRNN神經(jīng)網(wǎng)絡(luò)方法來進(jìn)行評(píng)價(jià),提出PCA-GRNN神經(jīng)網(wǎng)絡(luò)方法。從實(shí)證研究上,首先利用MIV算法對(duì)貴州省社會(huì)發(fā)展水平指標(biāo)體系的經(jīng)濟(jì)發(fā)展、社會(huì)進(jìn)步、生態(tài)環(huán)境、資源產(chǎn)消四個(gè)子系統(tǒng)的指標(biāo)分別進(jìn)行篩選,然后利用熵權(quán)法、敏感權(quán)法分別逐層進(jìn)行賦權(quán),通過比較綜合指數(shù)法、TOPSIS法、及P=W*R幾種綜合評(píng)價(jià)結(jié)果的偏差度大小,本文采用偏差度最小的P=W*R模型進(jìn)行綜合賦值,得到了貴州省從1996—2013年社會(huì)發(fā)展綜合水平綜合值,分析其18年貴州省社會(huì)發(fā)展水平趨勢(shì)變化情況。采用同樣的方法對(duì)西南地區(qū)幾個(gè)省市分別進(jìn)行指標(biāo)的篩選、提取各省共同保留下來的指標(biāo)進(jìn)行綜合評(píng)價(jià),對(duì)西南地區(qū)各省市(西藏除外)的社會(huì)發(fā)展綜合水平進(jìn)行比較和差異性分析,分析了四省在經(jīng)濟(jì)發(fā)展、社會(huì)進(jìn)步、生態(tài)環(huán)境各系統(tǒng)上每年綜合水平變化率的情況,動(dòng)態(tài)的分析四省今后的發(fā)展趨勢(shì),最后結(jié)合特色指標(biāo)著重的分析了貴州省經(jīng)濟(jì)發(fā)展、社會(huì)進(jìn)步、生態(tài)環(huán)境各系統(tǒng)動(dòng)態(tài)變化趨勢(shì),展現(xiàn)了貴州未來發(fā)展前景,提出相應(yīng)了協(xié)助措施及其政策性建議。
[Abstract]:Since the 20th century, the national economy has developed at a high speed, but at the same time many social problems have been gradually exposed. For example, the economic development has brought about the problem of overproduction of many products, and the average life expectancy of the population has increased. But with the emergence of the problem of social security for the elderly population; the speed of economic development has accelerated, the competition has become fierce, and the problem of unfair social distribution has also emerged; while the industrial development has been accompanied by the waste water produced in industrial engineering, Exhaust emissions, environmental problems followed by. Wait These problems show that the economic development can not represent the comprehensive level of development of a country or region, whether the real healthy development. The theory of the level of social development was put forward, and many scholars began to study this aspect. How to define the connotation of social development and what evaluation method should be adopted to evaluate the social development level of a country or region has become a problem of great concern in the field of theory and practice at present. At present, there are no internationally recognized systems and methods for the comprehensive evaluation of the level of social development. In view of the rich meaning of social development, statistical means are always limited, and there is no absolute perfect measurement and evaluation method in reality. Only by rethinking, rethinking and correctly grasping the scientific connotations and principles of social development can we further explore scientific statistical methods suitable for social development. In order to describe the situation of social development as objectively as possible. This paper evaluates the level of social development in Guizhou Province by combining theoretical research with empirical research. In this paper, the connotation of social development, the selection and selection of indicators, and the comprehensive evaluation method are analyzed theoretically. Finally, a four-level index system model is established. Based on the limitation of the traditional statistical method, this paper selects the GRNN neural network method with nonlinear fitting function to evaluate, and puts forward the PCA-GRNN neural network method. In the empirical research, firstly, the index of the index system of the level of social development in Guizhou Province is screened by MIV algorithm, and the indexes of the four subsystems, namely, economic development, social progress, ecological environment, resource production and consumption, are screened respectively, and then the entropy weight method is used. The sensitive weight method is weighted layer by layer. By comparing the synthetic index method with the TOPSIS method and the degree of deviation of several kinds of comprehensive evaluation results of P=W*R, this paper adopts the P=W*R model with the minimum deviation degree to assign the value synthetically. The comprehensive values of social development level in Guizhou Province from 1996 to 2013 are obtained and the trend of social development level in Guizhou Province during the past 18 years is analyzed. By using the same method, several provinces and cities in southwest China were selected for index selection, and the indexes retained by each province were extracted for comprehensive evaluation. The comprehensive level of social development in provinces and cities in southwest China (except Tibet) is compared and the differences are analyzed, and the annual comprehensive level change rates of the four provinces in economic development, social progress and ecological environment systems are analyzed. Dynamic analysis of the four provinces in the future development trends, finally combined with the characteristics of the indicators focused on the analysis of Guizhou's economic development, social progress, ecological environment system dynamic trends, showing the future development prospects of Guizhou. The corresponding assistance measures and their policy recommendations are put forward.
【學(xué)位授予單位】：貴州民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP183;D67

【相似文獻(xiàn)】

相關(guān)會(huì)議論文 前10條

1 銀濤;俞集輝;;基于GRNN的電力系統(tǒng)短期負(fù)荷預(yù)測(cè)[A];第十屆全國電工數(shù)學(xué)學(xué)術(shù)年會(huì)論文集[C];2005年

2 Ziwen Leng;Junwei Gao;Yong Qin;Xin Liu;Jing Yin;;Short-term Forecasting Model of Traffic Flow Based on GRNN[A];第25屆中國控制與決策會(huì)議論文集[C];2013年

3 陳其紅;闞樹林;秦臻;;基于廣義回歸神經(jīng)網(wǎng)絡(luò)(GRNN)的設(shè)備可靠性預(yù)測(cè)[A];2011年全國機(jī)械行業(yè)可靠性技術(shù)學(xué)術(shù)交流會(huì)暨第四屆可靠性工程分會(huì)第三次全體委員大會(huì)論文集[C];2011年

4 柴毅;凌睿;;基于參數(shù)優(yōu)化與GRNN逼近的非線性PID控制[A];第二十六屆中國控制會(huì)議論文集[C];2007年

5 徐中;張敬瑩;趙小波;;基于GRNN的粘彈材料阻尼性能的預(yù)測(cè)[A];第六屆中國功能材料及其應(yīng)用學(xué)術(shù)會(huì)議論文集（10）[C];2007年

6 王小輝;王琪潔;;基于廣義回歸神經(jīng)網(wǎng)絡(luò)的日長(zhǎng)變化的高精度預(yù)報(bào)[A];中國天文學(xué)會(huì)2011年學(xué)術(shù)年會(huì)手冊(cè)[C];2011年

7 馬珊;龐永杰;張鐵棟;;基于GRNN的聲圖像特征研究[A];第十五屆中國海洋（岸）工程學(xué)術(shù)討論會(huì)論文集(上)[C];2011年

8 陳端;曹陽;梅一韜;仲云飛;吳邦彬;;GRNN神經(jīng)網(wǎng)絡(luò)在大壩滲流預(yù)測(cè)中的應(yīng)用[A];2012年中國水力發(fā)電工程學(xué)會(huì)大壩安全監(jiān)測(cè)專委會(huì)年會(huì)暨學(xué)術(shù)交流會(huì)論文集[C];2012年

9 李慧英;李曉奇;;旅游需求預(yù)測(cè)分析——對(duì)比GRNN與多元回歸分析方法的應(yīng)用[A];第四屆中國智能計(jì)算大會(huì)論文集[C];2010年

10 陳欣;肖建華;欒培賢;徐強(qiáng);王洪斌;;基于BP與GRNN神經(jīng)網(wǎng)絡(luò)的PRRS預(yù)測(cè)模型的研究[A];中國畜牧獸醫(yī)學(xué)會(huì)信息技術(shù)分會(huì)2012年學(xué)術(shù)研討會(huì)論文集[C];2012年

相關(guān)碩士學(xué)位論文 前7條

1 趙煥;輕涂紙涂布量測(cè)定方法的實(shí)驗(yàn)研究[D];天津科技大學(xué);2013年

2 董江偉;風(fēng)速相似性形態(tài)研究及其在短期風(fēng)速預(yù)測(cè)中的應(yīng)用[D];南京信息工程大學(xué);2016年

3 陳曦;基于ARCH-因子分析-GRNN組合技術(shù)的中國跨國公司匯率風(fēng)險(xiǎn)預(yù)測(cè)模型構(gòu)建[D];北京外國語大學(xué);2016年

4 張宇;一種新型GRNN神經(jīng)網(wǎng)絡(luò)的制冷壓縮機(jī)銷售預(yù)測(cè)研究[D];上海交通大學(xué);2015年

5 羅安飛;貴州省社會(huì)發(fā)展水平評(píng)價(jià)研究[D];貴州民族大學(xué);2016年

6 金帥軍;基于GRNN神經(jīng)網(wǎng)絡(luò)的農(nóng)作物蟲害量預(yù)測(cè)系統(tǒng)設(shè)計(jì)[D];內(nèi)蒙古工業(yè)大學(xué);2013年

7 任茹香;基于GRNN的變權(quán)重組合預(yù)測(cè)模型在傳染病發(fā)病率預(yù)測(cè)中的應(yīng)用[D];浙江大學(xué);2011年

，

本文編號(hào)：2195792