Combination of Particle Swarm Optimization with LSSVM for Pipeline Defect Reconstruction
作者单位：Harbin Engineering University
会议名称：《2015年中国智能自动化学术会议》
会议日期：2015年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(Grant No.51409062,51279036) the Fundamental Research Funds for the Central Universities(HEUCF041530)
关 键 词：Pipeline Magnetic flux leakage 2D defect reconstruction PSO Least squares support vector machines
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.