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Recursive least squares algorithm and gradient algorithm for Hammerstein-Wiener systems using the data filtering
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DOI：10.1007/s11071-015-2548-5.png)
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Abstract
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This paper considers the parameter estimation problems of Hammerstein-Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the data filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein-Wiener systems.
Keywords:
Parameter estimation
Least squares
Gradient search
Data filtering
Nonlinear system
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