One Step Iterative Strategy for Nonlinear System Identification

In view of the difficulty of modeling for complex nonlinear system, a novel one step iterative identification algorithm for the linear part of the system is proposed in this paper, based on Taylor series expansion. The effect of sample on the precision of the model was analyzed by utilizing rigorous mathematical theory, and neuro-fuzzy model was used to identify the Taylor remainder and noise. To verify the efficiency of the proposed algorithm, it was applied to a classical benchmark batch process. The algorithm proposed here has a good performance and provides a new way for the modeling of complex nonlinear system.