發(fā)布時間：2018-06-21 15:18

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

【摘要】：我國現(xiàn)在正處于政府機構(gòu)改革、政府職能轉(zhuǎn)變的關(guān)鍵時期,機構(gòu)和編制的決策必須適應(yīng)社會主義市場經(jīng)濟的框架。而現(xiàn)階段,仍有很多編制管理部門依然沿用趨勢分析法、經(jīng)驗比例法等“老方法”進行編制預(yù)測,造成了編制核定工作的人為性、隨意性、主觀性,以及政府各部門之間編制申請增加、行政編制虛多、互相攀比等問題。因此,研究新形勢下的編制總量預(yù)測方法就十分有意義。目前編制總量預(yù)測的主要方法有：經(jīng)驗比例法、趨勢分析法、回歸分析法、馬爾可夫法、灰色預(yù)測方法等,國內(nèi)已有專家學者對編制預(yù)測這個問題進行了探索性的研究,從政治、經(jīng)濟、管轄面積、管轄人口、文化傳統(tǒng)、道德風尚等因素分析,建立了政府人員配置模型。 本文提出了一種改進的RBF網(wǎng)絡(luò)算法,并結(jié)合當?shù)貙嶋H,選取河南省信陽市獅河區(qū)(政府級別為縣區(qū)級)作為研究對象,將經(jīng)濟、民生等相關(guān)指標一并納入到編制核定的指標體系中來,如：政府轄區(qū)的面積、人口、行政區(qū)劃、國內(nèi)生產(chǎn)總值、工農(nóng)業(yè)產(chǎn)值、財政收人、行政經(jīng)費等。選取獅河區(qū)歷年的人口規(guī)模(人)X1,經(jīng)濟水平(萬元)X2,地域面積(平方公里)X3,財政收入(億元)X4,民間非政府組織X5,公職人員年齡占比分布X6,公職人員學歷占比分布X7作為訓練樣本進行訓練學習對泖河區(qū)政府編制總量進行了預(yù)測。本文的創(chuàng)新點主要體現(xiàn)在如下兩個方面。 1.提出了基于RBF神經(jīng)網(wǎng)絡(luò)的縣區(qū)級地方政府編制總量的預(yù)測方法。 2.對傳統(tǒng)RBF網(wǎng)絡(luò)模型在如下兩方面進行了改進： (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時僅根據(jù)經(jīng)驗進行學習,本文引入了GCV準則進一步優(yōu)化寬度參數(shù)σ。 (2)對RBF網(wǎng)絡(luò)進行子網(wǎng)絡(luò)化優(yōu)化處理。 預(yù)測結(jié)果表明：基于改進的RBF網(wǎng)絡(luò)算法對縣(區(qū))級地方政府編制總量的預(yù)測,比傳統(tǒng)的編制總量預(yù)測方法誤差更小,表現(xiàn)出精度更高的預(yù)測效果。這種編制總量預(yù)測方法比趨勢分析法、經(jīng)驗比例法等“老方法”的系統(tǒng)性和科學性更強,因此,地方政府及編制管理部門在進行編制決策時能夠以此作為重要依據(jù),具有一定的推廣應(yīng)用價值。
[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.
【學位授予單位】：信陽師范學院
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：D630;TP18

【參考文獻】

相關(guān)期刊論文 前10條

1 孝感市財政局課題組;;財政供養(yǎng)系數(shù)的關(guān)聯(lián)分析與降低供養(yǎng)系數(shù)的路徑選擇——湖北省72縣(市)和孝感市財政供養(yǎng)系數(shù)的實證分析[J];財政與發(fā)展;2005年01期

2 姚榮斌;李生權(quán);;關(guān)于最近鄰聚類的RBF網(wǎng)絡(luò)自整定PID控制算法的研究[J];工業(yè)儀表與自動化裝置;2007年06期

3 徐國浪;魏延;;基于多核函數(shù)的模糊支持向量機學習算法[J];重慶師范大學學報(自然科學版);2012年06期

4 夏克文;李昌彪;沈鈞毅;;前向神經(jīng)網(wǎng)絡(luò)隱含層節(jié)點數(shù)的一種優(yōu)化算法[J];計算機科學;2005年10期

5 梁斌梅;韋琳娜;;改進的徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)預(yù)測模型[J];計算機仿真;2009年11期

6 曾凡培;;粗集神經(jīng)網(wǎng)絡(luò)在網(wǎng)絡(luò)入侵中的應(yīng)用研究[J];計算機仿真;2011年07期

7 魏海坤;李奇;宋文忠;;梯度算法下RBF網(wǎng)的參數(shù)變化動態(tài)[J];控制理論與應(yīng)用;2007年03期

8 周義程;;公共利益、公共事務(wù)和公共事業(yè)的概念界說[J];南京社會科學;2007年01期

9 楊興紅;梁昌勇;朱龍;;地方行政編制總量影響因素實證研究——以安徽省縣級政府為例[J];華東經(jīng)濟管理;2012年12期

10 馬樹才,胡立杰,王威;地方行政、事業(yè)機構(gòu)編制配置與總量調(diào)控研究[J];統(tǒng)計研究;2005年09期

，

本文編號：2049211