Abstract
In this paper, we study the parameter estimation problem of a class of output nonlinear systems and propose a recursive least squares (RLS) algorithm for estimating the parameters of the nonlinear systems based on the model decomposition. The proposed algorithm has lower computational cost than the existing over-parameterization model-based RLS algorithm. The simulation results indicate that the proposed algorithm can effectively estimate the parameters of the nonlinear systems.
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This work was supported by the National Natural Science Foundation of China (No. 61203111) and the PAPD of Jiangsu Higher Education Institutions.
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Ding, F., Wang, X., Chen, Q. et al. Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition. Circuits Syst Signal Process 35, 3323–3338 (2016). https://doi.org/10.1007/s00034-015-0190-6
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DOI: https://doi.org/10.1007/s00034-015-0190-6