Data filtering-based parameter estimation algorithms for a class of nonlinear systems with colored noises

This article studies the data filtering-based identification algorithms for a class of nonlinear system with autoregressive noise. By means of the data filtering technique and the hierarchical identification principle, the identification model is transformed into two sub-identification models, and a filtering hierarchical gradient-based iterative algorithm is proposed for improving parameter estimation accuracy and reducing computational burden. Meanwhile, to further improve the identification performance, the multi-innovation identification theory is used to derived the filtering hierarchical multi-innovation gradient-based iterative algorithm. The gradient-based iterative algorithm is given for comparison. The analysis shows that the filtering hierarchical gradient-based iterative algorithm has smaller computational burden and can give more accurate parameter estimates than the gradient-based iterative algorithm, and the filtering hierarchical multi-innovation gradient-based iterative algorithm can track time-varying parameters based on the dynamical window data. Finally, the example part is provided to verify the effectiveness of the proposed algorithms.