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叠层递归径向基网络在客户流失分析中的应用

王华秋 邹航 阎河   

  1. 重庆工学院计算机学院 重庆工学院计算机学院 重庆工学院计算机学院
  • 收稿日期:2008-05-29 修回日期:2008-07-24 发布日期:2008-11-01 出版日期:2008-11-01
  • 通讯作者: 王华秋

Application of laminated recursive RBF network in loss of customers

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  • Received:2008-05-29 Revised:2008-07-24 Online:2008-11-01 Published:2008-11-01
  • Contact: Hua-qiu WANG

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摘要： 将叠层递归径向基(RBF)网络应用于客户流失研究，给出了客户流失研究中涉及的主要问题的解决方案，包括客户描述、属性规约、流失原因分析以及流失预测与控制策略等，解决了流失预测模型的建立问题，并采用单元叠层输入技术实现了神经网络的动态存储，这使网络具有了动态地处理数据的能力, 提出自适应距离优化核函数的中心半径参数，对混沌时序数据的仿真实验证明算法的有效性。最后利用训练后的模型对每个客户的流失可能性进行预测以及生成可能流失的客户列表，通过对实际案例的应用，对模型的有效性和可操作性进行了验证，证明其具有实际意义。

关键词: 叠层, 递归径向基网络, 自适应距离, 客户流失, 预测模型

Abstract: In this paper the laminated recursive Radius Basis Function (RBF) network was applied to the loss of customers, and the solutions of the main issues involved in the loss of customers were presented to address the loss forecast model of the problem, including customer description attributes statute, the reasons for the loss prediction and control strategies. The unit laminated technology was adopted into the recursive RBF network to achieve the dynamic storage, which made the network have a dynamic data processing capability. The self-adaptive distance to optimize the kernel function bandwidth was also presented. The simulation on the temporal data from Mackey-Glass proves the effectiveness of the algorithm. Finally, the trained model was used to predict each customer's loss of possibility and generate the loss list of customers. Through the application to actual cases, the effectiveness and feasibility of the model are verified.

Key words: laminate, recursive Radius Basis Function (RBF), self-adapted distance, loss of customers, prediction model

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