Bank Customer Classification Model Based on Elman Neural Network Optimized by PSO

摘要：

Bank customer classification plays an important role for commercial banks to keep away from default risks in customer loan market. This paper constructs a bank customer classification model based on Elman neural network. Aiming at the insufficiency of BP algorithm used in standard Elman neural network, this paper combines the PSO algorithm and the Elman neural network to construct a PSO-Elman neural network by using PSO as the training method. The model was used in bank customer classification based on customer loan data from one commercial bank. Compared with the standard Elman network trained by BP algorithm, the application results indicate that PSO-Elman network gets higher classification accuracy on testing samples and performs better on robustness.

关键词： particle swarm optimization Elman network customer classification

作者: Guang Yang Xu-chuan Yuan

作者单位: School of Management Harbin Institute of Technology Harbin, China

会议类型: 国际会议

会议名称: 第三届IEEE无线通讯、网络技术暨移动计算国际会议

会议地点: 上海

会议语种:英文

在线出版日期: 2007-09-21（万方平台首次上网日期，不代表论文的发表时间）