本文選題：RBF + 神經(jīng)網(wǎng)絡(luò)��； 參考：《信陽(yáng)師范學(xué)院》2015年碩士論文

【摘要】：我國(guó)現(xiàn)在正處于政府機(jī)構(gòu)改革、政府職能轉(zhuǎn)變的關(guān)鍵時(shí)期,機(jī)構(gòu)和編制的決策必須適應(yīng)社會(huì)主義市場(chǎng)經(jīng)濟(jì)的框架。而現(xiàn)階段,仍有很多編制管理部門(mén)依然沿用趨勢(shì)分析法、經(jīng)驗(yàn)比例法等“老方法”進(jìn)行編制預(yù)測(cè),造成了編制核定工作的人為性、隨意性、主觀性,以及政府各部門(mén)之間編制申請(qǐng)?jiān)黾�、行政編制虛多、互相攀比等�?wèn)題。因此,研究新形勢(shì)下的編制總量預(yù)測(cè)方法就十分有意義。目前編制總量預(yù)測(cè)的主要方法有：經(jīng)驗(yàn)比例法、趨勢(shì)分析法、回歸分析法、馬爾可夫法、灰色預(yù)測(cè)方法等,國(guó)內(nèi)已有專家學(xué)者對(duì)編制預(yù)測(cè)這個(gè)問(wèn)題進(jìn)行了探索性的研究,從政治、經(jīng)濟(jì)、管轄面積、管轄人口、文化傳統(tǒng)、道德風(fēng)尚等因素分析,建立了政府人員配置模型。 本文提出了一種改進(jìn)的RBF網(wǎng)絡(luò)算法,并結(jié)合當(dāng)?shù)貙?shí)際,選取河南省信陽(yáng)市獅河區(qū)(政府級(jí)別為縣區(qū)級(jí))作為研究對(duì)象,將經(jīng)濟(jì)、民生等相關(guān)指標(biāo)一并納入到編制核定的指標(biāo)體系中來(lái),如：政府轄區(qū)的面積、人口、行政區(qū)劃、國(guó)內(nèi)生產(chǎn)總值、工農(nóng)業(yè)產(chǎn)值、財(cái)政收人、行政經(jīng)費(fèi)等。選取獅河區(qū)歷年的人口規(guī)模(人)X1,經(jīng)濟(jì)水平(萬(wàn)元)X2,地域面積(平方公里)X3,財(cái)政收入(億元)X4,民間非政府組織X5,公職人員年齡占比分布X6,公職人員學(xué)歷占比分布X7作為訓(xùn)練樣本進(jìn)行訓(xùn)練學(xué)習(xí)對(duì)泖河區(qū)政府編制總量進(jìn)行了預(yù)測(cè)。本文的創(chuàng)新點(diǎn)主要體現(xiàn)在如下兩個(gè)方面。 1.提出了基于RBF神經(jīng)網(wǎng)絡(luò)的縣區(qū)級(jí)地方政府編制總量的預(yù)測(cè)方法。 2.對(duì)傳統(tǒng)RBF網(wǎng)絡(luò)模型在如下兩方面進(jìn)行了改進(jìn)： (1)優(yōu)化寬度σ取值。以往在確定函數(shù)中心寬度參數(shù)σ時(shí)僅根據(jù)經(jīng)驗(yàn)進(jìn)行學(xué)習(xí),本文引入了GCV準(zhǔn)則進(jìn)一步優(yōu)化寬度參數(shù)σ。 (2)對(duì)RBF網(wǎng)絡(luò)進(jìn)行子網(wǎng)絡(luò)化優(yōu)化處理。 預(yù)測(cè)結(jié)果表明：基于改進(jìn)的RBF網(wǎng)絡(luò)算法對(duì)縣(區(qū))級(jí)地方政府編制總量的預(yù)測(cè),比傳統(tǒng)的編制總量預(yù)測(cè)方法誤差更小,表現(xiàn)出精度更高的預(yù)測(cè)效果。這種編制總量預(yù)測(cè)方法比趨勢(shì)分析法、經(jīng)驗(yàn)比例法等“老方法”的系統(tǒng)性和科學(xué)性更強(qiáng),因此,地方政府及編制管理部門(mén)在進(jìn)行編制決策時(shí)能夠以此作為重要依據(jù),具有一定的推廣應(yīng)用價(jià)值。
[Abstract]:Our country is now in the government organization reform, the government function changes the key period, the organization and the establishment decision must adapt to the socialist market economy frame. However, at the present stage, there are still many establishment management departments that still use "old methods" such as trend analysis, experience-proportional methods, etc., to carry out compilation and prediction, resulting in the artificial, arbitrary and subjective nature of the compilation and approval work. As well as government departments between the establishment of applications, the administrative establishment of more false, compared with each other, and so on. Therefore, it is of great significance to study the method of forecasting the total amount of compilation under the new situation. At present, the main methods of compiling total forecast are: empirical proportion method, trend analysis method, regression analysis method, Markov method, grey forecast method and so on. This paper analyzes the factors of politics, economy, jurisdiction area, governing population, culture tradition, moral custom and so on, and establishes the model of government staffing. In this paper, an improved RBF network algorithm is proposed, and the Shihe District of Xinyang City, Henan Province (government level is county level) is selected as the research object. People's livelihood and other related indicators are incorporated into the approved index system, such as: the area of government jurisdiction, population, administrative divisions, GDP, industrial and agricultural output value, financial revenue, administrative funds, and so on. Select the population scale of Shihe District in the past years (people X 1, economic level (10 000 yuan x 2, geographical area x 3 square kilometers, revenue 1 million yuan X 4, non-governmental organizations X 5, age ratio of public officials X 6, educational background of public officials x 6%) X 7 was used as a training sample to predict the total amount of government establishment in Maohe district. The innovation of this paper is mainly reflected in the following two aspects. 1. Based on RBF neural network, the prediction method of the total amount of local government at county and district level is put forward. 2. The traditional RBF network model is improved in the following two aspects: 1) optimizing the width 蟽. In the past, the function center width parameter 蟽 was studied only according to experience. In this paper, the GCV criterion is introduced to further optimize the width parameter 蟽. The prediction results show that the prediction of the total amount of local government at the county (district) level based on the improved RBF neural network algorithm is less than that of the traditional method, and the prediction effect is higher than that of the traditional method. This method is more systematic and scientific than the old methods, such as trend analysis, experiential proportion, etc. Therefore, local governments and establishment management departments can take this as an important basis for making decisions. It has certain value of popularization and application.
【學(xué)位授予單位】：信陽(yáng)師范學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：D630;TP18

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本文編號(hào)：2049211