现代制造工程Issue(7)：46-49,4.
基于粒子群算法优化支持向量机的数控机床状态预测
State prediction for CNC machine based on PSO-SVM
许志军1
作者信息
- 1. 四川机电职业技术学院,攀枝花,617000
- 折叠
摘要
Abstract
Prediction of CNC machine is significant to find out the health state of CNC machine. To forecast CNC machine exactly,Support Vector Machine optimized by Particle Swarm Optimization algorithm(PSO-SVM) is proposed to forecast the health state of CNC machine. Particle swarm optimization algorithm is used to determine the training parameters of support vector machine in this model, which can gain optimized SVM forecasting model. The experimental results indicate that the proposed PSO-SVM model not only requires small training data,but also can achieve great accuracy.关键词
支持向量机/参数优化/数控机床/预测模型Key words
Support Vector Machine ( SVM )/ parameter optimization/ CNC machine/ forecasting model分类
管理科学引用本文复制引用
许志军..基于粒子群算法优化支持向量机的数控机床状态预测[J].现代制造工程,2011,(7):46-49,4.
