Abstract: The strip elongation is difficult to predict accurately with mathematical model， which related with multi-variable nonlinear factors and data noise in the annealing process. Thus， the optimal soft-sensing method was proposed based on kernel principal component analysis (KPCA) and optimized least squares support vector machine (LSSVM) by immune clone particle swarm optimization (ICPSO). ICPSO can avoid the particles sinking into premature convergence and running into local optimization in the iterative process which was generated by particle swarm optimization (PSO) algorithm， and can also optimize the parameters of LSSVM. Then， KPCA was used to denoise the input data set and capture the high-dimensional nonlinear principal components in input data space， and the principal components were input into the ICPSO-LSSVM model to establish the soft-sensing prediction model. The proposed method was successfully applied to the strip elongation prediction in annealing furnace. The simulation results show that the KPCA and ICPSO-LSSVM model have higher prediction accuracy， compared with other algorithms.

Key words: kernel principal component analysis, strip elongation, immune clone particle swarm optimization, least squares support vector machine, soft-sensing