Optimal Initial State for Fast Parameter Estimation in Nonlinear Dynamical Systems

Abstract

This paper deals with optimal initial state for parameter estimation in a bounded error context. Based on sensitivity analysis, our original method uses this information to evaluate the optimal initial state start point for precise parameter estimation task. In our framework, the uncertainty (on measurement noise and parameters) is considered as to be interval, thus guaranteed sensitivity analysis method has been applied which ensure to obtain a better parameter estimation. Once the system's initial state is determined, a set inversion procedure combined with a volumetric criterion's contractor is proposed. The proposition of check the necessity of the contractor's action moment is new. Besides, we point out that the set membership computation should have the measurement points as few as possible, an elementary effect analysis in the interval analysis context is also implemented to achieve the fast and guaranteed parameter estimation aim.