小微企业信用风险评估的IDGSO-BP集成模型构建研究

针对传统BP神经网络在小微企业信用风险评估实际应用中,随机初始权值和阈值导致网络学习速度慢、易陷入局部解以及运算结果误差较大等缺陷,借助群智能萤火虫(GSO)算法,提出一种基于改进离散型萤火虫(IDGSO)算法的BP神经网络集成学习算法的小微企业信用风险评估IDGSO-BP模型。该模型以BP神经网络为基本框架,在学习过程中引入离散型萤火虫算法,优化设计神经网络的网络结构与连接权值,得到一组相对合适的权值与阈值,再进行新一轮网络训练,以“均平方误差最小”为评价准则,产生网络的输出结果,以此建立小微企业信用风险评估模型。其仿真实验结果表明,该模型在收敛速度及运算精度方面较传统BP神经网络模型、遗传GA-BP模型及连续GSO-BP模型有较明显优势。因此,IDGSO-BP模型可以有效提高小微企业信用风险评估的准确性。     

基于网络客户评论的产品口碑排序方法

电子商务时代,产品的网络口碑已成为消费者做购买决策的重要参考依据。本文利用客户评论信息,依据产品的评分及产品之间的对比投票数据,提出了一个新的产品口碑排序方法。首先利用产品两两间的对比投票计算各自的相对口碑,再采用贝叶斯平均方法修正原始客户评分,然后将二者结合得到产品的总口碑,进而对产品的网络口碑进行排序。实验数据采自第三方点评网站中的产品对比投票数据,实验结果表明本文提出的产品口碑排序方法具有较高的支持度,且与产品销量排序的相关性也很高。     

二级学院实施“大类培养”模式的探索与实践

针对学分制背景下,现行的“专业招生,专业培养”教学管理模式存在的不足,提出了在二级学院试点实施“大类培养”的新模式;并在修订教学计划、整合教学资源、优化师资配置和小班教学等4个方面进行了实践与探索,同时提出了学院今后应努力的方向。     

基于不均衡数据的小企业信用风险评价

小企业信用风险评价既是银行风险管理问题,又事关经济社会稳定。针对小企业贷款实践中,违约样本远少于非违约样本、且违约客户误判对银行影响较大的现实,采用不均衡支持向量机对小企业信用风险评价指标进行赋权,进而构建了能有效区分违约客户、非违约客户的评价模型。根据有无特定评价指标、特定评价指标数值变化对贷款小企业违约状态的影响程度赋权;反映了对违约状态影响越大、评价指标权重越大的赋权思路。将违约样本正确识别率、违约样本的准确率与查全率等因素作为支持向量机赋权模型中客户识别率的度量标准,改变了样本数据不均衡所导致的样本总体精度很高、违约样本精度反而不高的现象。研究结果表明:行业景气指数、资本固定化比率、净利润现金含量、恩格尔系数、营业利润率等评价指标对小企业信用风险的影响较大。     

creditrisk~+模型在信贷组合信用风险度量中的应用研究

creditrisk~+模型以泊松分布为理论基础,是一种输入数据较少、计算复杂度较小、便于在银行实际中应用和推广的信贷组合信用风险度量模型.本文在分析国外creditrisk~+模型频带划分缺陷基础上,改用加权平均的频带划分方法,并提出了新的违约率参数的确定办法,利用creditrisk~+模型对大连市商业银行某支行224笔中小公司贷款的非预期损失进行了实证计算.结果发现:creditrisk~+模型可以有效地计量信贷组合的非预期损失且可提高我国商业银行经济资本管理效率.     

关于信用期内含缺陷产品EOQ模型

考虑到在实际中供应链上游供应商提供给下游零售商的信用支付期通常为一个订货周期,建立了缺陷率服从一定分布的缺陷产品在信用支付策略下的最优订货批量模型.模型中允许缺货发生并且以最大期望利润为目标函数,通过分析得到模型最优解.最后给出仿真实验,并且分析了模型参数变化对最优解的影响.     

污染环境中Gallopin系统的生存分析

研究了环境污染对Ｇａｌｌｏｐｉｎ资源－消费系统中消费种群的长期影响,给出了种群弱平均持续生存和绝灭的判据,在一定条件下得到了阈值。     

湖南省城镇居民消费结构的实证分析

文章以湖南统计年鉴提供的数据资料为依据,建立了扩展的线性支出系统模型,利用该模型,计算了湖南省城镇居民年基本需求支出量、边际消费倾向、需求收入弹性和需求自价格弹性。根据这些计算结果分析了湖南省城镇居民消费结构中存在的问题,并提出了相应的对策。     

An inventory model under two levels of trade credit and limited storage space derived without derivatives

This paper tries to incorporate both Huang’s model [Y.F. Huang, Optimal retailer’s ordering policies in the EOQ model under trade credit financing, J. Oper. Res. Soc. 54 (2003) 1011–1015] and Teng’s model [J.T. Teng, On the economic order quantity under conditions of permissible delay in payments, J. Oper. Res. Soc. 53 (2002) 915–918] by considering the retailer’s storage space limited to reflect the real-life situations. That is, we want to investigate the retailer’s inventory policy under two levels of trade credit and limited storage space. Furthermore, we adopt Teng’s viewpoint [J.T. Teng, On the economic order quantity under conditions of permissible delay in payments, J. Oper. Res. Soc. 53 (2002) 915–918] that the retailer’s unit selling price and the purchasing price per unit are not necessarily equal. Then, an algebraic approach is provided and three easy-to-use theorems are developed to efficiently determine the optimal cycle time. Some previously published results of other researchers can be deduced as special cases. Finally, a numerical example is given to illustrate these theorems and managerial insights are drawn.     

Economic order quantity under conditionally permissible delay in payments

Within the economic order quantity (EOQ) framework, the main purpose of this paper is to investigate the retailer’s optimal replenishment policy under permissible delay in payments. All previously published articles dealing with optimal order quantity with permissible delay in payments assumed that the supplier only offers the retailer fully permissible delay in payments if the retailer ordered a sufficient quantity. Otherwise, permissible delay in payments would not be permitted. However, in this paper, we want to extend this extreme case by assuming that the supplier would offer the retailer partially permissible delay in payments when the order quantity is smaller than a predetermined quantity. Under this condition, we model the retailer’s inventory system as a cost minimization problem to determine the retailer’s optimal inventory cycle time and optimal order quantity. Three theorems are established to describe the optimal replenishment policy for the retailer. Some previously published results of other researchers can be deduced as special cases. Finally, numerical examples are given to illustrate all these theorems and to draw managerial insights.