摘要
信贷对现代市场经济非常重要,同时又会带来风险.以人工智能的思想为指导,将SOM与PNN网络相结合,提出并建立了一种基于SOM-PNN的信贷风险预警模型;结合统计理论方法对输入样本进行预处理,解决了网络训练中样本选用的问题;并利用因素分析方法对预警结果进行了解释.实验表明,利用该模型在得到可视化预测结果的同时,还可得到较高的预警精度.
The risk prediction model is very critical and regarded as the core part of the risk early warning system. Compared with other traditional prediction models, the neural network model has the advantages of self-studying, self-organizing as well as self-adapting. This paper presents a SOM-PNN based risk prediction model, which combines the Self-Organizing Map Neural Network and the Probabilistic Neural Network together. Futhermore, the improved iteration ways in constructing and training the model are also presented, which includes the SOM boundary effect processing and the rare samples handling. The established model is trained with financial ratios for a specific credit risk early warning experiment. The preliminary experimental result demonstrates that the SOM-PNN model perfoms better than some traditional ones in the rates of prediction accuracy and efficiency.
出处
《小型微型计算机系统》
CSCD
北大核心
2005年第9期1571-1574,共4页
Journal of Chinese Computer Systems
基金
北京市教育委员会科技发展计划(KM200410028012)资助.
