Fitting Nonlinear Signal Models Using the Increasing-Data Criterion
Jimei Li, Feng Ding
Abstract
To extract important information about the nonlinear signals, this letter makes the utmost of the fitting advantages of Gaussian and polynomial functions, and proposes a nonlinear signal model with broader applications. Then we focus on the parameter estimation issues of the proposed models in the presence of noises. The stability factor recursive algorithm is devised based on the increasing noisy data, which makes full use of the information from the nonlinear signals. Applying the hierarchical identification principle, a two-stage recursive algorithm with higher computational efficiency is developed for the nonlinear signals. The simulation results test the effectiveness of the proposed algorithms from the aspects of estimation accuracy and prediction effect.