本文選題：社會(huì)保障　切入點(diǎn)：神經(jīng)網(wǎng)絡(luò)　出處：《湖南農(nóng)業(yè)大學(xué)》2014年碩士論文

【摘要】：湘鄉(xiāng)市是位于湖南省中部的一個(gè)重要工業(yè)基地和休閑旅游城市,下轄18個(gè)鄉(xiāng)鎮(zhèn)4個(gè)辦事處,其中以普通農(nóng)民占多數(shù)。在這樣一個(gè)縣級(jí)市中推行統(tǒng)籌推進(jìn)“城鄉(xiāng)社會(huì)保障體系建設(shè)”,貫徹十八大提出的“全覆蓋、�；�、多層次、可持續(xù)”的12字方針,“以增強(qiáng)公平性、適應(yīng)流動(dòng)性、保證可持續(xù)性為重點(diǎn)”,建成覆蓋城鄉(xiāng)居民的社會(huì)保障體系是一個(gè)巨大的挑戰(zhàn)。特別是不同的人群對(duì)社會(huì)保障體系中養(yǎng)老保險(xiǎn)繳費(fèi)檔次存在極大的差異,如何根據(jù)個(gè)人的工作、家庭收入等特征推薦最適合的養(yǎng)老保險(xiǎn)等級(jí)是一個(gè)非常值得研究的問題。針對(duì)上述問題,本文結(jié)合現(xiàn)代計(jì)算機(jī)技術(shù),基于人工神經(jīng)網(wǎng)絡(luò),利用個(gè)人信息智能推薦符合參保對(duì)象的養(yǎng)老保險(xiǎn)等級(jí),其主要工作包括： (1)基于參保人群和社保等級(jí)信息模型,從人群的地域?qū)傩�、家庭屬性、工作屬性等多個(gè)維度出發(fā),構(gòu)建了一套社保人群多維信息體系,并對(duì)個(gè)體信息進(jìn)行編碼和數(shù)學(xué)建模。 (2)設(shè)計(jì)了湘鄉(xiāng)市社保人群本體需求評(píng)價(jià)與識(shí)別特征的關(guān)系樹,通過運(yùn)行多維向量空間模型來表示參保對(duì)象的進(jìn)行分類的特征,通過余弦定理來對(duì)特征向量的相似度進(jìn)行計(jì)算,對(duì)參保對(duì)象進(jìn)行研究,在保證有效分類的前提下,保證需收集的信息最少。 (3)設(shè)計(jì)了一種基于個(gè)體特征向量的神經(jīng)網(wǎng)絡(luò)分類算法,并根據(jù)現(xiàn)有的社保系統(tǒng)中的樣本數(shù)據(jù)進(jìn)行仿真訓(xùn)練和修正。實(shí)驗(yàn)表明,本文算法能較為準(zhǔn)確的識(shí)別社保人群所需的養(yǎng)老保險(xiǎn)費(fèi)等級(jí),相比傳統(tǒng)的直接推薦和自主選擇,有效的提高了針對(duì)性。驗(yàn)證了算法對(duì)參保人員分類的可行性,并研究個(gè)體信息樣本對(duì)神經(jīng)網(wǎng)絡(luò)算法的影響。 (4)設(shè)計(jì)了參保人群社保等級(jí)需求分類系統(tǒng)框架,該框架有效地將本文提出的多維編碼、參保對(duì)象需求評(píng)價(jià)樹、個(gè)人信息識(shí)別特征優(yōu)化、神經(jīng)網(wǎng)絡(luò)分類算法融合為一體,并分別進(jìn)行了UML分析,列出了核心代碼和運(yùn)行界面。
[Abstract]:Xiangxiang is an important industrial base and leisure tourism city in the central part of Hunan Province, with 18 township and 4 offices under its jurisdiction. Among them, ordinary peasants account for the majority. In such a county-level city, we should carry out the "urban and rural social security system construction" as a whole and implement the 12-character policy of "full coverage, basic protection, multi-level and sustainable" proposed by the 18th CPC National Congress, "in order to enhance fairness." Adapting to mobility and ensuring sustainability is a major challenge. "building a social security system that covers urban and rural residents is a huge challenge. In particular, there are significant differences between different groups of people in terms of the level of pension insurance contributions in the social security system," he said. How to recommend the most suitable old-age insurance grade according to the characteristics of individual work and family income is a problem worth studying. In view of the above problems, this paper combines modern computer technology, based on artificial neural network, Using personal information intelligence to recommend the pension insurance grade according to the insured object, its main work includes:. 1) based on the information model of insured population and social security grade, a set of multi-dimensional information system of social security population is constructed based on the regional attribute, family attribute and job attribute of the population, and the individual information is coded and modeled by mathematics. (2) the relationship tree between the needs evaluation and identification features of social security population in Xiangxiang city is designed. The multi-dimensional vector space model is run to represent the classification features of insured objects, and the similarity of feature vectors is calculated by cosine theorem. On the premise of ensuring effective classification, the information to be collected is the least. (3) A neural network classification algorithm based on individual feature vector is designed, and the simulation training and modification are carried out according to the existing sample data of social security system. The experimental results show that, Compared with the traditional direct recommendation and independent selection, the algorithm can identify the pension insurance level required by the social security population accurately, and effectively improve the pertinence. The feasibility of the algorithm to classify the insured is verified. The influence of individual information samples on neural network algorithm is also studied. The framework of the social security classification system for insured population is designed. The framework effectively integrates the multi-dimensional coding, the object requirement evaluation tree, the feature optimization of personal information identification, and the neural network classification algorithm proposed in this paper. UML analysis is carried out, and the core code and running interface are listed.
【學(xué)位授予單位】：湖南農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP183;TP391.3

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