摘要
为协助设计师能精准地为客户提供有效的产品服务系统整体解决方案,针对其客户流失预测问题,提出了一种改进粒子群算法与支持向量机相结合的客户流失预测方法(IPSO-SVM)。该方法包括构建了产品服务系统客户流失模型及IPSO-SVM算法模型。首先,IPSO-SVM算法采用粒子位置表示支持向量机的参数,并基于Sobol序列对粒子群位置与速度初始化,然后位置更新时引入动态自适应非线性惯性权重的方法。最后,以某高档数控机床公司客户流失状态为案例,通过与BPNN、SVM、PSO-SVM进行比较,验证所提方法在该数控机床产品服务系统客户流失模型中的有效性与可行性。
To assist the designer accurately to provide the effective solution of product service system for customers,according to the customer churn prediction problem,a kind method of customer churn prediction combining an improved particle swarm algorithm with support vector machine（IPSO-SVM） was proposed.The method includes constructing the customer churn model of product service system and the IPSO-SVM algorithm model.Firstly,the particle position is used to represent the parameters of SVM,and the position and velocity of particle swarm are initialized based on Sobol sequence.Finally,through compared with BPNN,SVM,PSO-SVM,the feasibility and validity of the introduced method was verified by appling in Customer churn prediction model of product service system for CNC Machine Tools.
作者
王涛
丛茜
尚钰量
程辉
张在房
樊蓓蓓
WANG Tao;CONG Qian;SHANG Yu-liang;CHENG Hui;ZHANG Zai-fang;FAN Bei-bei(Shanghai Aerospace Equipment Manufacturer, Shanghai 200245, China;School of Mechatronic and Au- tomation Engineering, Shanghai University, Shanghai 200072, China)
出处
《组合机床与自动化加工技术》
北大核心
2018年第5期181-184,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学基金项目(51205242
51405281
16111106402)
关键词
产品服务系统
客户流失
支持向量机
粒子群优化算法
product service system
customer chum
support vector machine
particle swarm optimizationalgorithm
