Combination of Particle Swarm Optimization with LSSVM for Pipeline Defect Reconstruction

摘要：

　　The nuclear function parameter and penalty parameter are pivotal factors which decide performance of Least Squares Support Vector Machines(LSSVM).Usually,most users select parameters for an LSSVM by rule of thumb,so they frequently fail to generate the optimal approaching effect for the function.In order to get optimal parameters automatically,a new approach based on particle swarm optimization and LSSVM was proposed,which automatically adjusts the parameters for LSSVM,ensuring the accuracy of parameter selection.This method was applied to pipeline 2D defect reconstruction; simulation results showed the method can overcome the difficulty of magnetic flux leakage signals,described defect geometrical characteristics,improving the reconstruction accuracy and practical value.

关键词： Pipeline Magnetic flux leakage 2D defect reconstruction PSO Least squares support vector machines

作者: Huixuan Fu Yuchao Wang Sheng Liu

作者单位: Harbin Engineering University,Nantong Street 145,Harbin,China

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 229-236

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）