摘要
通过构建信用担保产品风险评价指标体系,应用神经网络技术对信用担保产品的风险进行综合评价;并通过在单指标评价标准范围内随机取值方法,生成建立神经网络模型所需的训练样本、检验样本和测试样本,建立可靠的信用担保产品风险BP网络综合评价模型。通过16个实例研究表明:提出的样本生成方法是可靠的,并能有效地避免出现"过训练"和"过拟合"现象,建立的BP模型具有较好的泛化能力,不受人为因素的影响。
On the basis of the constructed evaluation index system of credit guarantee risk, the neural network technology is applied to comprehensive risk assessment of the credit guarantee products. The training samples, verifica- tion samples and testing samples that the model needed are generated by random sampling method in the range of single- index evaluation standard. The case study indicates that the methodology for generating samples and the process for establishing BP-ANN model are effective and reliable. The phenomenon of over-training and over-fitting can be effectively avoided, and the BP model possesses good generalization and is not influenced by human factors.
出处
《湖南工业大学学报》
2013年第2期68-73,共6页
Journal of Hunan University of Technology
基金
教育部人文社会科学研究青年基金资助项目(11YJCZH054)
湖南省教育厅高等学校科学研究基金资助项目(11C0439)
湖南工业大学社会科学基金资助项目(2011HSX05)
关键词
BP神经网络
信用担保
风险评价
back propagation neural network model
credit guarantee
risk evaluation
