Study on Commercial Bank Risk Early Warning System Based on UDM and Self-Adaptive RBFNN

摘要：

By using scientific uniform design method,the representative and uniformity samples are designed. Thus,the multi-factors and multi-levels self- learning training is arranged using limited experiments times,and the self-adaptive RBFNN are adopted to realize the bank risk early warning diagnosis. Experiments show the results between self- adaptive RBFNN evaluation and experts fuzzy comprehensive evaluation (FCE) are very close,The generalization ability of self-adaptive RBFNN with UDM is far better than that of traditional RBFNN with Monte-Carlo method The self-adaptive RBFNN with UDM realizes non-linear approaching ability of evaluation,meantime conquers the capability limitation of traditional RBFNN and BP neural network,and avoids the subjectivity and uncertainty of traditional FCE.

关键词： bank risk early warning radial basis function neural network (RBFNN) uniform design method (UDM) nearest neighbor-clustering algorithm (NNCA) fuzzy comprehensive evaluation (FCE)

作者: KANG Shi-ying

作者单位: School of Computer Science and Information Technology,Chongqing Technology and Business University,Chongqing 400067,P. R. China

会议类型: 国际会议

会议名称: The First International Conference on Management Innovation(ICMI 2007)(管理创新会议)

会议地点: 上海

会议语种:英文

页码: 430-435

在线出版日期: 2007-06-04（万方平台首次上网日期，不代表论文的发表时间）