Application of soft sensor in welding seam tracking prediction based on LSSVM and PSO with compression factor

There are some problems with the strip steel heated in the annealing furnace such as the multiple correlations between temperature in the annealing furnace, steel strip tension, tension roll speed and other variables, noise in field data, which lead to the difficulty to predict the time of welding seam achieved air-knife. The least square support vector machine (LSSVM) inductance model optimized by the particle swarm optimization with compression factor (PSO-CF) algorithm is presented for the difficulty of the time prediction in this paper. The improved algorithm can improve PSO convergence accuracy, and effectively avoid falling into local optimum. It can be both the global fitting ability and local fitting ability of least squares support vector machine. The parameters of LSSVM model are optimized by improved PSO-CF algorithm to escape from the blindness of man-made choice. Using the algorithm in prediction of the arrival time and the position of welding seam, the numerical simulation results illustrate good generalization performance and prediction ability of the proposed method.