SOM神经网络模型在商业银行信用风险评估中的应用研究

研究了神经网络技术在商业银行信用风险评估中的应用,结合主成分分析法和SOM人工神经网络,建立了商业银行信用风险评估的人工神经网络模型;实证结果表明,该模型具有较高的预测精度.     

基于改进BP神经网络的心电信号分类方法

心电信号的准确判别是实现心电监测系统智能诊断的关键。为提高心电信号的分类精度,研究了一种改进BP神经网络的心电信号分类算法。首先对MIT-BIH Arrhythmia Database样本专家标注心拍进行统计分析,选择正常心拍、室性早搏、左束支传导阻滞心拍和右束支传导阻滞心拍作为神经网络识别目标,采用主成分分析法提取25个心拍特征作为样本向量。仿真结果表明,改进BP神经网络具有较好的分类识别能力,整个样本分类准确率为98. 4%。算法收敛速度快,分类精度高,有助于检测和诊断心脏疾病。     

基于卷积神经网络和贝叶斯分类器的句子分类模型

针对传统句子分类模型存在特征提取过程复杂且分类准确率较低等不足,利用当下流行的基于深度学习模型的卷积神经网络在特征提取上的优势,结合传统句子分类方法提出一种基于卷积神经网络和贝叶斯分类器的句子分类模型。该模型首先利用卷积神经网络提取文本特征,其次利用主成分分析法对文本特征进行降维,最后利用贝叶斯分类器进行句子分类。实验结果表明在康奈尔大学公开的影评数据集和斯坦福大学情感分类数据集上,所提出的方法优于只使用深度学习的模型或传统句子分类模型。     

基于主成分的SFLABP网络模型在帕金森氏病分类中的应用

针对BP神经网络学习效率低、容易陷入局部最优等缺点,提出了一种基于主成分分析的混合蛙跳算法(Shuffle FrogLeaping Algorithm)优化的BP神经网络模型。使用主成分分析法对高维数据进行特征提取,作为网络输入;采用混合蛙跳算法优化BP神经网络的权系数和阈值,构建基于混合蛙跳算法神经网络的帕金森病分类模型。最后,以UCI中Parkinson数据为例,实验表明,新模型优于传统的BP网络。     

主成分分析与神经网络结合的黄山毛峰茶品质检测

研究了利用主成分分析与BP神经网络结合的方法对黄山毛峰茶进行品质检测。首先应用主成分分析法对反映茶叶香气信息的原始特征变量进行分析,提取出前5个主成分,再以这些主成分作为BP神经网络的输入,建立3层BP神经网络预测模型。试验结果表明,该模型相对于未经过主成分分析的BP神经网络模型,建模效率大大提高,判别准确率也由92.5%提高到97.5%。说明主成分分析与BP神经网络结合应用于黄山毛峰茶品质检测是有效的。     

商业银行信用风险评估方法研究

研究商业银行信用风险准确评估问题,由于商业银行信贷资金安全存在不确定性,信用风险评估指标较多,指标间存在大量重复信息,风险等级与指标间呈非线性关系,导致传统评估模型很难精确地进行评估,评估精度不高.为了提高商业银行信用风险评估精度,提出一种将层次分析法(AHP)与BPNN(BPNN)相结合的的商业银行信用风险评估模型(AHPBPNN).模型首先利用层次分析法求出各指标的权重,并按照权重的大小进行指标排序,消除指标的重复信息,使评估指标得到了精简,然后将经过处理后的指标输入BPNN,通过进行非线性学习和建模,最后对信用风险进行评估仿真.实验结果表明,AHP-BPNN简化了评估指标体系,提高了评估的速度和精度,增加了商业银行信用风险评估的有效性.     

组合模型在商业银行信用风险评估中的研究

研究商业银行信用风险评估问题,商业银行信用风险评评估涉及指标相当多,各指标间呈非线性关系且存在严重冗余信息,传统评估方法不能很好消除冗余信息,只能反映指标间的线性关系,导致风险评估准确率低.为了提高商业银行信用风险评估的准确性,提出了一种粗糙集理论(Rs)和BP神经网络(BPNN)相结合的商业银行信用风险评估组合模型(RS_BPNN).新模型首先利用粗糙集理论对各评估指标进行指标约筒,消除指标间的冗余消息,简化神经网络的网络结构,然后将约简后的数据输入非线性预测能力优异的BP神经网络进行训练,得到商业银行信用风险评估模型,最后采用中国工商银行某分行数据对组合模型进行仿真试验.仿真结果表明,与传统的BP神经网络模型相比,组合模型加快了网络的运算速度,提高风险评估准确率,获得评估结果更具科学性.     

苹果近红外光谱的特征提取和分类研究

采用近红外光谱分析法对不同种类的苹果样品进行分类,提出一种基于非相关判别转换的苹果近红外光谱定性分析新方法。实验分别采用主成分分析、Fisher判别分析和非相关判别转换三种方法对苹果光谱数据进行特征提取,并使用K-近邻分类算法建立三种苹果分类识别模型,最后使用"留一"交叉验证法进行模型检验。结果表明,使用非相关判别转换方法建立的模型正确识别率优于使用主成分分析和Fisher判别分析建立的模型。     

基于PCA优化的神经网络飞机燃油消耗预测方法

针对传统方法存在的不足,提出了基于主成分分析法优化的Elman神经网络飞机燃油消耗预测方法。利用主成分分析法降低神经网络输入维数。构建主成分分析与Elman神经网络模型,进行基于飞参数据的实例分析,并将几种神经网络的预测效果进行了对比;提出了基于K-S检验法预测结果冗余修正法并进行了修正。误差指标和预测图像表明与主成分分析结合后Elman神经网络对飞机燃油消耗的预测性能优于其他传统神经网络,且K S检验法能够有效实现对预测结果的修正。     

自适应概率神经网络及其在白酒电子鼻中的应用

为了探索电子鼻对白酒品质鉴别的可能性,利用自制的新型无线白酒电子鼻对洋河海之蓝、今世缘省接待、安徽迎驾大曲和牛栏山陈酿进行了分析.对所采集的数据进行平滑处理后提取稳态响应值和斜率值,利用主成分分析对特征向量进行降维处理,并将获得的前2个主元得分作为概率神经网络识别模型的输入参量.针对传统概率神经网络平滑因子σ单一易导致分类错误的缺陷,利用差异演化算法优化σ参数集,建立了自适应概率神经网络识别模型.实验结果表明,DE-PNN相比BP-PNN、PSO-PNN和SVM等,识别精度更高,抗噪性能更好,同时也证明了电子鼻能有效地检出不同品牌的白酒.     

Neural nets versus conventional techniques in credit scoring in Egyptian banking

Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank’s personal loans’ data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques.     

商业银行信用风险评估实证分析及方法比较

数据挖掘技术为商业银行信用风险管理问题提供了新的思路和方法。本文运用三种常用的数据挖掘方法——多元判别分析、聚类分析及贝叶斯网络模型,以商业银行的客户信用风险评级指标数据为样本,对信用风险评估方法进行实证分析,对三种方法的验证结果进行比较。结论表明,在信用风险各项属性指标之间条件相互依赖的情况下,贝叶斯网络模型优于其它两种方法。     

基于灰色系统理论的商业银行竞争力评价模型

建立了市场占有能力、盈利性等4方面的竞争力评价指标体系，引入灰色系统理论，建立了基于灰色系统理论的商业银行竞争力评价模型，该模型具有如下主要特点：一是构建理想银行。计算被评价银行与理想银行之间的关联度，其关联度大小的排序即银行竞争力强弱的顺序，二是选取典型银行，进行了主成分分析和灰色关联优势分析，通过综合关联度的计算得到各指标对综合得分的影响程度，解决了现有研究中对指标权重确定主观性太强的缺点。     

基于稀有事件仿真的商业银行个人信用风险评估研究

系统仿真是风险评价的一种重要手段,针对商业银行个人信用风险预警问题,提出一种基于稀有事件仿真的个人信用风险评估方法.采用商业银行个人未偿还贷款的概率作为衡量个人信用风险高低的标准,构造基于稀有事件的商业银行个人信用风险识别模型,利用交叉熵方法构建了一种稀有事件仿真的有效算法,并由此估计出发生损失的概率.实证分析结果表明,模型对商业银行个人信用风险具有很强的识别能力,从而提供了一个风险预警的新视角.     

人工神经网络在个人住房贷款信用评价中的应用

个人住房贷款在商业银行的业务中属于高风险的一种,因此,对贷款申请人进行信用评价极为重要。本文运用matlab软件,把人工神经网络与信用评价系统结合起来,贷款申请人的各项指标作为输入值,信用度为输出值,为科学评价个人信用提供了依据。     

The individual borrowers recognition: Single and ensemble trees

Banks provide a financial intermediary service by channeling funds efficiently between borrowers and lenders. Bank lending is subject to credit risk when loans are not paid back on a timely basis or are in default. The ability or possessing a methodology to evaluate the creditworthiness of a borrower is therefore crucial to managing the bank’s risk management and profitability.The aim of the paper is dichotomous classification of the individual borrowers to the groups of creditworthy or non-creditworthy clients. The recognition of borrowers is provided applying single and aggregated classification trees.Classification trees are a powerful alternative to the more traditional statistical models. This model has the advantage of being able to detect non-linear relationships and showing a good performance in presence of qualitative information as it happens in the creditworthiness evaluation of individual borrowers. As a result, they are widely used as base classifiers for ensemble methods.Aggregated classification trees are constructed employing two ensemble methods: Adaboost and bagging. AdaBoost constructs its base classifiers in sequence, updating a distribution over the training examples to create each base classifier. Bagging combines the individual classifiers built in bootstrap replicates of the training set.The research is conducted employing actual data regarding the individual borrowers that got a mortgage credit in one of the commercial banks that operate in Poland. Each of the clients is described by 11 variables. The grouping variable informs if the client pays off the credit regularly due to the credit agreement or he is back in loan redemption. Diagnostic variables describe the clients in terms of demographic features and characterize the credits that are to be paid back (i.e. value and currency of the credit, credit rate, etc.).     

中小型商业银行的个人征信报送平台智能优化模型设计

针对目前中小型商业银行特别是城市商业银行在报送个人征信数据所遇到的种种问题进行现实客观的分析，提出了搭建个人征信报送平台的智能模型设计方案所遵循的相关原则。通过直观的模型框架图进行展现并对报送平台的智能模型各个关键数据处理环节进行了详细的标注，同时提供了个人征信数据抽取的Job工作流图来进行功能上的辅助说明，为各个中小型城市商业银行在搭建自己的征信数据报送平台提供了一套有价值的实施参考模板。     

神经网络的企业信用风险评估应用研究

研究企业信用风险评估准确性问题,企业存在产品质量、不良贷款等信用风险问题,企业信用风险是多种因素的综合结果,存在着不确定、非线性、随机性等特点,无法建立确定数学评估模型。只能根据专家评估指标为依据。为了提高企业信用风险评估准确率,提出一种BP神经网络的企业信用风险评估方法。先采用层次分析法构建风险评估指标体系,再用专家系统对评估指标进行量化打分,最后采用BP神经网络对企业信用风险指标进行非线性学习,并对企业信用风险等级进行评估。实验结果表明,BP神经网络的企业信用风险评估模模型能显著提高评估准确率,并能够反映企业信用风险的随机性变化特点,使评估结果更加符合实际情况,为企业信用风险评估提供了参考。     

Constructing credit auditing and control & management model with data mining technique

The 2008 financial tsunami, hitting the globe across all types of industries, causing tides of bankruptcies and severe unemployment, had its epicenter at American subprime in the housing market. In fact, the US subprime storm was just a premonition, while the root cause of the financial tsunami lied in the oversupply of structured credit products. Credit card business, one of the structured credit products, which under an intensively competitive environment, have been released by many banks with high spread, high return, and easy-to-apply appeals to carter to consumers needs. In order to allure the customers, some banks even go to the extent as simplify the credit rating, which in turn has increased credit risk, causing high non-performing ratio, increased debt collection cost, and growing bad debt counts. Accordingly, credit risk auditing plays a vital role in the successful management of credit card business. In response to such needs, the present study aims to conduct analysis and investigation on the current status of the industry with CRISP-DM model. First, customers’ demographic data and payment-related statistics were analyzed to identify feature variables, which were then sorted out as demographic data, debt data, payment rating etc. Next, by utilizing artificial neural network of data mining technique, the study tries to predict customer’s regular pattern of consumption, payment and/or default and bad debt, and to develop a set of credit granting principle by employing the decision tree technique. Since data mining classification model has a greater power in discriminating credit card granting, it can thus be used to construct accurate credit variable rules and predictive model, to further improve credit checking effect and credit risk control. Using the credit auditing data of a certain bank as a case study, the study intends to verify that the model constructed by the researcher can effectively identify the potential key factors of its credit card granting rule, to minimize the cost loss of Model I and Model II credit business, and eventually enhance the stability and profitability of the bank’s credit card business.