Pipeline defect reconstruction based on improved particle swarm optimization with LSSVM

Abstract

Aiming at complex characteristics of magnetic flux leakage signal, and it is hard to describe geometric features of pipeline defect. A novel approach based on improved particle swarm optimization and Least Squares Support Vector Machines (LSSVM) algorithm was proposed to reconstruct pipeline defect in this paper. The nuclear function parameter and penalty parameter are vital factors which determine performance of Least Squares Support Vector Machines. Use improved particle swarm optimization optimize the parameters of LSSVM automatically, insuring the accuracy of parameter choice. This method was used to pipeline defect reconstruction, simulation results demonstrate that method can break through the difficulty of magnetic flux leakage signals described defect geometrical characteristics, improving the reconstruction accuracy, with a highly practice value.