High Accuracy Behavior Prediction of Nonlinear Dynamic System with Semi-Parametric Model-Based Signal Separation

Abstract

The behavior prediction of nonlinear dynamic system is a challenging problem, especially when the system includes many independent subsystems. The observations from the complex dynamic system are the result of the interaction of multiple dynamic subsystems, which results in a loss of predictability. In this paper, semi-parametric model-based signal separation technique, in which validity function with penalizing is used to estimate the component number of the Gaussian mixture model (GMM) for every hidden source signal, is adopted to separate the observations of complex nonlinear dynamic system in order to improve its predictability. Then local support vector regression (SVR) technique is used to model the separated observations and make prediction. Finally, the prediction results are remixed as the original observation prediction or the behavior prediction of the complex nonlinear dynamic system. The experimental results show that the proposed method can separate the observation of the complex dynamic system robustly, improve the prediction accuracy substantially and perform better than the other comparison methods.