Application of Ant Colony Optimization-SVM in Fault Diagnosis for Rectifier Circuit

摘要：

Failure of rectifier circuit has the characteristics of latency and complexity,which leads to the difficulty to fault diagnosis for rectifier circuit.A new method of optimizing support vector machine (SV.V1) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the paper.The experimental object is provided and the six ACO-SVM classifiers are developed to identify the following seven states of the experimental object.The testing results demonstrate that the ACO-SVM classifier has higher diagnostic accuracy than normal support vector machine and BP neural network.

关键词： rectifier circuit ant colony optimization classifier fault diagnosis classification algorithm

作者: Xu Binghui

作者单位: Taizhou Vocational & Technical College Taizhou 318000,China

会议类型: 国际会议

会议名称: 2010 2nd IEEE International Conference on Information and Financial Engineering(2010年第二届IEEE信息与金融工程国际会议 ICIFE 2010)

会议地点: 重庆

会议语种:英文

页码: 594-597

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）