基于遗传算法和BP神经网络的信用风险测量模型被引量：8

The Model of Credit Risk Measurement based on GA and BP NN

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摘要随着新巴赛尔协议的推出,信用风险测量问题越来越受到重视。在西方发达国家,商业银行的信用风险管理测量技术已比较成熟,继传统的比例分析、主观分析之后,统计方法得到广泛的应用,如判别分析和logit回归分析等。自从20世纪80年代末期以来,人工智能技术如神经网络、专家系统等也被应用于商业银行信用风险测量中。目前,该领域应用最多的就是BP神经网络,但其固有的一些缺点,如易陷入局部极小点,会影响预测效果。但可以利用改进的遗传算法对BP神经网络进行优化,实验证明效果非常好。 Credit risk measurement becomes more important with the new Basel Capital Accord＇sh publicizing. In developed countries, the technologies of credit risk measurement are mature. After traditional scale analysis and subjective analysis, statistic methods are used widely, for example discriminant analysis and Logit regression analysis. Artificial intelligence technologies as Neural Network and expert systems are also used in the field from 1980g. Now Back - Propagation Neural Network is used the most frequently in this field. But it has some shortcomings as likelihood of getting in local point. So effect will be impacted. This paper optimizes Back - Propagation Neural Network with improved Genetic Algorithm, and the result is good.

作者王宪全陈李刚

机构地区哈尔滨工业大学管理学院

出处《哈尔滨工业大学学报（社会科学版）》 2006年第5期87-92,共6页 Journal of Harbin Institute of Technology(Social Sciences Edition)

关键词信用风险遗传算法 BP神经网络 credit risk genetic algorithm back propagation neural network

分类号 F830.5 [经济管理—金融学]

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