Support Vector Machine based parameter identification and diminishment of parametric drift

Support Vector Machine is applied to the modeling of a nonlinear dynamic system. Linear kernel is adopted in sample training and the parameters in the mathematical model are calculated by resultant lagrangian factors and support vectors. To diminish the parameter drift in identification, training samples are reconstructed by difference method. Correlation analysis demonstrates the validity of reconstruction. Based on the regressive mathematical model, the dynamics of the system is predicted and comparison between predicted results and test results confirms the parameters identified.