基于极限学习机的出口食品加工企业检验检疫信用评价研究

为全面有效地掌握及整合企业信用信息,国家质检总局建立了一套进出口企业信用管理系统,制定了企业信用管理办法及评价标准.文章在分析出口食品加工企业检验检疫信用评价指标体系的基础上,建立了一种基于极限学习机的检验检疫信用评价模型.实验结果证明,该模型可有效预测企业信用等级,仅需预先确定隐含层神经元数目而无需设置其他参数,减少了人为干扰因素,可为检验检疫信用评价管理提供参考.     

东莞出口蔬菜装运可全程视频监控

近日获悉,包括5家种植菜场和4家加工企业及2个铅封点在内的东莞市蔬菜出口企业,目前都已实现点对点视频监控。检验检疫部门通过远程视频监控系统,可对出口蔬菜装运,铅封的过程进行实时监控,并可以对蔬菜装车全过程进行视频录制以方便日后查阅或取证。     

基于改进型BP神经网络的信用评估系统研究

通过研究企业信用评估中的模型问题，为企业经营活动和决策过程提供信息支持。介绍了几种常用的信用评估模型，通过分析它们在评估中的缺陷，提出基于改进型BP神经网络的信用评估模型。在建立指标体系和输出机制的基础上，讨论了基于信用评估模型的评估系统的设计与实现。对模型和评估系统的不足进行了分析，并提出了改进建议。     

浅谈加强进口空箱检疫监管的思考和建议

随着进出口贸易的不断发展,入境空箱疫情的表现形式和种类均在不断变化,原有闸口查验模式逐渐暴露出其局限性和不足.从出口加工区检验检疫的实际出发,探讨了进口空箱检疫监管,以期能为今后的出口加工区检验检疫工作提供借鉴.     

湛江：视频监控网助进出境货物快速核放

湛江检验检疫局围绕“提速、减负、增效和严密监管”的目标，全面推进检验检疫监督管理模式改革，对重点出口企业和重点进出境敏感商品实施了视频监控，加快了进出境货物的快速核放，对进出境敏感商品的质量把关也发挥了极其重要的作用。     

基于UDDI的语义Web信用评估服务研究

UDDI服务注册机的服务发现提供了简单的基于关键字的搜索方法,但缺乏对语义推理的支持。论文在对UD鄄DI服务发现基本模型的研究基础上加入了语义推理,并运用到电子商务信用评估服务发现中。提出了基于UDDI的语义Web信用评估服务系统的结构,着重介绍了基于本体论的电子商务信用评估元模型,并利用信用评估元模型构造信用服务原子模型。     

基于B/S结构的融资信用评估系统研发

本文结合我国信用体系建设和解决企业因“信用危机”而“融资难”紧迫需要,在分析融资信用评估系统需求基础之上,结合IT技术发展趋势,提出了基于B／S结构的融资信用评估系统体系结构,建立并分析其系统功能模型、评价体系和关键技术。在此基础上,以SQL Server2000为后台数据库系统,以Java为前台开发工具,开发了一套基于EJB体系结构的融资信用评估软件原型系统。     

基于支持向量机的企业信用评估模型

在经济活动中,信用是一种建立在对受信人在特定期限内付款或还款承诺信任的的能力,也可以是受信人无须付现就可以获取商品、服务、资金的能力。企业在经济活动中,信用是至关重要的。本文提出了一种基于SVM(支持向量机,SupportVectorMachine)的企业信用评估模型,可以根据企业的相关数据对企业进行信用评估。     

基于朴素贝叶斯分类器的个人信用评估模型

个人信用评估是金融与银行界研究的重要内容。论文研究了三种朴素贝叶斯分类器信用评估模型的精度。在两个真实数据集上用10层交叉验证对朴素贝叶斯信用评估模型进行了测试,并与五种DavidWest的神经网络个人信用评估模型进行了对比。结果表明朴素贝叶斯分类器具有较低的分类误差,在信用评估中有优势。     

基于AdaBoost-BOA的中小企业信用评估模型

针对我国现有信贷风险评估体系的不完善以及银行对中小企业的信用等级评估的要求,提出了一种基于Ada?Boost-BOA的中小企业信用评估模型.首先确定中小企业信用评估指标,然后通过贝叶斯优化算法构建AdaBoost-BOA集成分类信用评估模型.实验结果表明,与其他传统的模型相比较,论文提出的AdaBoost-BOA模型在信用等级评估中具有更优良的评估性能,其准确率更高.     

HS编码查询知识库设计与实现

HS(Harmonized System)商品编码体系被进出口监管和统计部门广泛使用.HS编码的智能查询可以为进出口相关企业提供便利,对监管自动化和效率提供有效支持.基于本体的自动分类方法,描述了一种实现HS编码查询的知识库与推理方法,提供了知识库构建、标准和非标准商品名处理、置信度计算等技术细节,给出了原型系统和实验数据.     

Study on the country risk rating with distributed crawling system

This paper built a system containing the Distributed Crawling Module, the Database Module and the Analysis Module to collect a large number of objective data, clean the data and realize the business intelligence representation. The distributed crawling system includes the Distributed Crawling Module building from Hadoop, the Database Module by SQL Server and the Analysis Module by the SAS system. The first two modules support a distributed way to collect and convert non-structural data into structural data for the last module processing. Then, this paper extends research fields from theory to application about B&R, constructs rating system from the perspective of political risk, economic risk, financial risk, business environment risk and legal risk, establishes a 140 rating index, collects 46,200 sample data, and adopts Model of Principal Components Analysis, Analytic Hierarchy Process and Efficacy Function to access “the Belt & Road Initiative” 66 countries for 5 consecutive years of export credit insurance in the country risk rating. This paper also gives a detail explanation on 2015 rating results, showing that, Singapore wins the highest credit rating among the country; the credit rating of Latvia, Estonia, Slovakia, Turkey, Malaysia, Russia, Thailand and other countries is very high; Afghanistan, Ukraine, Laos, Iran, Arabia, Republic of Syria, Iraq, Burma, Republic of Yemen, East Timor and other countries with poor credit ratings. The conclusion is consistent with the domestic and overseas well-known rating agencies.

     

基于LM算法的企业资信网络评估模型研究

针对当前企业资信评估方法的不足,提出将基于Levenberg-Marquard(LM)算法的前向多层神经网络用于企业资信评估,并通过MATLAB神经网络工具对其进行模拟计算。实验结果表明,该方法稳定、快捷、预测准确,对企业资信评估有着良好的性能。     

一般模糊极大-极小神经网络的研究与应用

分析一般模糊极大-极小神经网络的基本原理,阐述模糊计算方法在分类中的准确性和高效性。将一般模糊极大-极小神经网络应用于企业资信评估中,实现模糊区间的输入,缩小企业评估指标定量化中的误差范围。资信评估结果表明,该算法能快速、有效地对企业进行分类,为资信评估提供了解决方案。     

A study of Taiwan's issuer credit rating systems using support vector machines

By providing credit risk information, credit rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries. In this paper, we propose an automatic classification model for issuer credit ratings, a type of fundamental credit rating information, by applying the support vector machine (SVM) method. This is a novel classification algorithm that is famous for dealing with high dimension classifications. We also use three new variables: stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification. Previous research has seldom considered these variables. The data period of the input variables used in this study covers three years, while most previous research has only considered one year. We compare our SVM model with the back propagation neural network (BP), a well-known credit rating classification method. Our experiment results show that the SVM classification model performs better than the BP model. The accuracy rate (84.62%) is also higher than previous research.     

基于蚁群算法的神经网络在企业资信评估中的应用

BP算法在资信评估中应用较为广泛，但有收敛速度慢、易于陷入局部极小点的缺点。提出一种新的企业资信评估模型，该模型将蚁群算法和神经网络结合起来，使其既具有神经网络的广泛映射能力，又有蚁群算法带来的高效率，全局收敛，分布式计算等特点。实验表明,基于蚁群算法的神经网络对企业资信评估有着良好的性能。     

基于MID 的检验检疫移动执法应用模型研究

针对现有移动执法系统在软件移植、功能实现及信息安全等方面存在不 足,利用MID 终端作为执法工具,提出了一种适用于检验检疫的移动执法应用模型。详细 描述了模型的分层机构组成,给出了一种适用于该模型的信息安全机制,并分别从终端侧、 网络侧、客户系统侧角度介绍了安全机制的实现方法。应用分析表明,该模型具有实用性和 有效性,可有效提升检验检疫信息化执法水平和执法效率。     

Expert system design for credit risk evaluation using neuro‐fuzzy logic

Over the past few years, the credit risk evaluation of micro‐, small‐ and medium‐scale enterprises by banks and financial institutions has been an active area of research under the joint pressure of regulators and shareholders. The credit rating assessment forms an important part of credit risk assessment, involving risk parameters such as financial, business, industry and management areas. The mathematical models of evaluation are at the core of modern credit risk management systems. This paper focuses on the use of fuzzy logic and neural network techniques to design a methodology for evaluating the credit worthiness of the entrepreneur. The neuro‐fuzzy logic approach takes into account the minute details of credit rating expert's thought process to arrive at the final decision. A flexible credit rating framework (CRF) has been designed to organize all the facts of the client in a hierarchical fashion. The neural networks provide self‐learning capability to the CRF. The CRF can be customized to suit different business and industrial interests.     

面向高维和不平衡数据的供应链金融信用评价

针对供应链金融模式下中小企业的信用风险控制问题,提出了一种面向高维和不平衡数据的信用风险预测模型。首先,基于Pearson-XGBoost两阶段特征选择建立供应链金融信用评价指标体系;其次,通过改进的NM-SMOTE算法对数据集进行平衡化;最后,利用Focal loss函数对XGBoost算法改进,并通过改进的粒子群算法进行优化,从而建立最终的信用评价模型。通过实验结果表明,提出的INS-IPSO-FLXGBoost模型对于中小企业具有更好的预测效果,可以更有效地识别风险企业。     

基于C2C的电子商务信用体系研究

Credit problem is the main bottleneck which hinders the development of e-commerce. This paper analyzes the current representative C2C credit evaluation models of Taobao and Youa, and proposes an improved model. The improved model uses a multi-standard evaluation system and new credit rating rules. And the evaluation algorithm considers the score of multi-standards, category and price of the commodity together, using a weighted system to calculate the credit score then to determine the credit rating. It solves the main problems which the current C2C credit rating algorithms haven't settled. In addition, the model puts forward some identification measures of false trading, the ID verification rules, and a third-party credit certification center, which partly solve the credit fraud problems arising from the credit island and credit speculation. Finally, the paper compares the improved model with current models to show its superiority.