本文選題：安全風(fēng)險　切入點：貝葉斯統(tǒng)計　出處：《華南理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：風(fēng)險研究是當(dāng)前研究的熱點問題，，如何定義安全風(fēng)險度量，使安全風(fēng)險度量能反映相關(guān)的差異信息，并可以通過相應(yīng)的分布特征預(yù)測和控制安全事故出現(xiàn)的可能性，是實際應(yīng)用中亟待解決的問題。 本文提出了適合高校資源配置安全風(fēng)險的新定義，通過對引起風(fēng)險差異信息的分布進(jìn)行研究，建立風(fēng)險評價與預(yù)測模型。該模型應(yīng)用在高校資源配置風(fēng)險問題上，依據(jù)投入產(chǎn)出績效、偏離期望績效差異、差異隨機(jī)出現(xiàn)的可能性等綜合評價資源配置過程可能導(dǎo)致的風(fēng)險程度，取得的預(yù)測效果良好。 本文的主要研究工作如下： (1)將安全風(fēng)險定義為各種影響因素的差異信息所引起的不確定因素，認(rèn)為安全風(fēng)險是受差異值與差異可能性的作用所引起的。在高校資源配置風(fēng)險問題中，基于績效評價模型，獲取2007-2011年72所高校的績效評價結(jié)果，以績效與期望績效（平均績效與最大績效）的偏離程度作為引起風(fēng)險的差異信息。 (2)利用貝葉斯估計差異信息的分布參數(shù)，動態(tài)研究績效偏離期望差異的分布變化特征，使能獲取差異分布參數(shù)估計量的變化規(guī)律，提高風(fēng)險程度預(yù)測的可靠性，使差異隨機(jī)出現(xiàn)可能性的預(yù)測偏差盡可能減少；在配置風(fēng)險問題中，高�？冃c期望的偏離情況是服從正態(tài)分布的，由此建立多層貝葉斯模型，動態(tài)獲取了每年高�？冃c期望績效偏離的分布，結(jié)果表示高校歷年績效與期望的偏離分布參數(shù)波動較小。 (3)利用K-Means聚類算法綜合差異信息和差異可能性等信息，尋求能代表不同風(fēng)險等級的中心樣本，采用高斯隸屬度函數(shù)以模糊隸屬度的形式描述7級風(fēng)險程度。此風(fēng)險評價模型以最大隸屬度對應(yīng)的風(fēng)險等級與綜合風(fēng)險等級作為高校的配置風(fēng)險評價結(jié)果，與高�，F(xiàn)狀較吻合。 (4)在風(fēng)險評價模型基礎(chǔ)上，建立基于BP神經(jīng)網(wǎng)絡(luò)的綜合風(fēng)險等級區(qū)間預(yù)測模型，用于風(fēng)險的動態(tài)預(yù)測。對高校的績效差異信息以及風(fēng)險評價結(jié)果建立預(yù)測模型，預(yù)測的準(zhǔn)確率達(dá)到了94.72%。
[Abstract]:Risk research is a hot issue in the current research. How to define the safety risk measurement so that the safety risk measurement can reflect the relevant difference information, and can predict and control the possibility of the safety accident through the corresponding distribution characteristics. It is an urgent problem to be solved in practical application. In this paper, a new definition of security risk suitable for university resource allocation is put forward. By studying the distribution of risk difference information, a risk evaluation and prediction model is established. The model is applied to the risk problem of resource allocation in colleges and universities. According to the input-output performance, deviating from the expected performance difference and the possibility of random occurrence of the difference, the risk degree that the resource allocation process may lead to is evaluated, and the prediction effect is good. The main work of this paper is as follows:. 1) the security risk is defined as the uncertain factor caused by the difference information of various influencing factors, and it is considered that the security risk is caused by the effect of the difference value and the difference possibility. In the problem of university resource allocation risk, it is based on the performance evaluation model. The results of performance evaluation of 72 colleges and universities from 2007 to 2011 were obtained, and the deviation between performance and expected performance (average performance versus maximum performance) was taken as the difference information of risk. 2) using Bayes to estimate the distribution parameters of the difference information, dynamic research on the distribution and variation characteristics of the performance deviating from the expected difference, so as to obtain the variation rule of the difference distribution parameter estimator, and improve the reliability of the risk degree prediction. In the problem of allocation risk, the deviation between performance and expectation in colleges and universities is normally distributed, and a multilayer Bayesian model is established. The dynamic distribution of the deviation between college performance and expected performance is obtained dynamically, and the results show that the deviation distribution parameters of performance and expectation in the past years of colleges and universities fluctuate little. (3) using K-Means clustering algorithm to synthesize the information of difference and possibility of difference, and to seek a central sample which can represent different risk levels. Gao Si membership function is used to describe the risk degree of grade 7 in the form of fuzzy membership degree, and the risk evaluation model takes the risk grade corresponding to the maximum membership degree and the comprehensive risk grade as the evaluation result of collocation risk in colleges and universities, which is consistent with the present situation of colleges and universities. 4) on the basis of risk evaluation model, a comprehensive risk grade interval forecasting model based on BP neural network is established, which is used for dynamic risk prediction. The performance difference information and risk evaluation results of colleges and universities are forecasted. The accuracy of prediction reached 94.72.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：G647

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