本文選題：BP神經(jīng)網(wǎng)絡(luò)　切入點(diǎn)：農(nóng)村中小金融機(jī)構(gòu)　出處：《安徽財(cái)經(jīng)大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：目前,我國對于農(nóng)村中小型金融機(jī)構(gòu)績效評(píng)估的研究還處于探索起步階段,多是借鑒國外商業(yè)銀行或微型金融機(jī)構(gòu)的評(píng)價(jià)體系,并且常用的財(cái)務(wù)績效評(píng)價(jià)方法都存在一定的欠缺。因此文章希望對農(nóng)村中小金融機(jī)構(gòu)的財(cái)務(wù)績效評(píng)價(jià)方法體系做一點(diǎn)改進(jìn),力圖在對農(nóng)村中小金融機(jī)構(gòu)績效評(píng)價(jià)方法研究的基礎(chǔ)上,結(jié)合我國農(nóng)村中小金融機(jī)構(gòu)的特殊性,提出一套適合于其特點(diǎn)和性質(zhì)的財(cái)務(wù)績效評(píng)價(jià)方法---BP神經(jīng)網(wǎng)絡(luò)。文章是首次嘗試著將財(cái)務(wù)績效評(píng)價(jià)與BP神經(jīng)網(wǎng)絡(luò)方法結(jié)合在一起對農(nóng)村中小金融機(jī)構(gòu)的經(jīng)營績效進(jìn)行分析,BP神經(jīng)網(wǎng)絡(luò)的自適應(yīng)性、自學(xué)習(xí)性、自組織性等特點(diǎn),合理地?cái)[脫了以往研究中人為因素及模糊隨機(jī)性的影響,其本身智能性的算法能給研究得到一個(gè)合理的評(píng)價(jià)結(jié)果。 那么,農(nóng)村中小金融機(jī)構(gòu)的經(jīng)營績效是高是低,如何評(píng)價(jià),這正是文章研究的目的。文章在對財(cái)務(wù)績效評(píng)價(jià)角度進(jìn)行明確的前提下,在了解國內(nèi)外關(guān)于績效評(píng)估理論發(fā)展歷程和深入研究的基礎(chǔ)上,并結(jié)合我國農(nóng)村中小金融機(jī)構(gòu)的性質(zhì)和特點(diǎn),建立農(nóng)村中小金融機(jī)構(gòu)財(cái)務(wù)績效評(píng)價(jià)指標(biāo)體系。在評(píng)價(jià)方法的選擇問題上,通過對各種常用業(yè)績評(píng)價(jià)方法進(jìn)行研究綜述和比較分析后,選擇用BP神經(jīng)網(wǎng)絡(luò)作為文章的財(cái)務(wù)績效評(píng)價(jià)方法,并在運(yùn)用中體現(xiàn)出BP神經(jīng)網(wǎng)絡(luò)與財(cái)務(wù)績效評(píng)價(jià)相結(jié)合的創(chuàng)新性,以及BP神經(jīng)網(wǎng)絡(luò)在文章研究中的優(yōu)越性。其次,在構(gòu)建好的農(nóng)村中小金融機(jī)構(gòu)財(cái)務(wù)績效評(píng)價(jià)模型基礎(chǔ)上進(jìn)行實(shí)證分析,通過對15家農(nóng)村中小金融機(jī)構(gòu)2009年樣本數(shù)據(jù)的學(xué)習(xí)訓(xùn)練,最終可以對5家2010年學(xué)習(xí)樣本數(shù)據(jù)進(jìn)行模擬運(yùn)算,并根據(jù)結(jié)果給樣本單位進(jìn)行排名。最后,對文章的研究工作進(jìn)行總結(jié),并對進(jìn)一步研究給出了展望和建議。具體分為五個(gè)部分全面系統(tǒng)地進(jìn)行深入研究。
[Abstract]:At present, the research on the performance evaluation of rural small and medium-sized financial institutions in China is still in the initial stage of exploration, most of them refer to the evaluation system of foreign commercial banks or micro-financial institutions. Therefore, the paper hopes to improve the financial performance evaluation system of small and medium-sized financial institutions in rural areas. On the basis of the research on the performance evaluation method of rural small and medium financial institutions, combined with the particularity of rural small and medium-sized financial institutions in China, This paper presents a set of financial performance evaluation methods suitable for its characteristics and properties-BP neural network. This paper is the first attempt to combine financial performance evaluation with BP neural network method to the management performance of rural small and medium-sized financial institutions. The self-adaptability of BP neural network is analyzed. The characteristics of self-study habit self-organization and so on are reasonable to get rid of the influence of human factors and fuzzy randomness in the previous research. The algorithm of its own intelligence can get a reasonable evaluation result for the research. So, the performance of rural small and medium-sized financial institutions is high or low, how to evaluate, this is the purpose of the article. On the basis of understanding the development course and in-depth research of performance evaluation theory at home and abroad, and combining the nature and characteristics of small and medium-sized financial institutions in rural areas, To establish the financial performance evaluation index system of small and medium-sized financial institutions in rural areas. On the selection of evaluation methods, through the research review and comparative analysis of various commonly used performance evaluation methods, The paper chooses BP neural network as the financial performance evaluation method of the article, and reflects the innovation of the combination of BP neural network and financial performance evaluation in the application, and the advantages of BP neural network in the article research. Secondly, Based on the financial performance evaluation model of rural small and medium-sized financial institutions, this paper analyzes the financial performance of 15 rural small and medium-sized financial institutions in 2009. In the end, we can simulate the data of five samples in 2010, and rank the sample units according to the results. Finally, we summarize the research work of this paper. The paper also gives the prospect and suggestion for further research, which is divided into five parts.
【學(xué)位授予單位】：安徽財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.35;F275;TP18

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