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Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition
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DOI：10.1007/s11071-015-2454-x.png)
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Abstract
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This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition-based least squares iterative algorithm is presented for estimating the parameter vector in each subsystem. Moreover, a data filtering-based decomposition least squares iterative algorithm is proposed. The simulation results indicate that the data filtering-based least squares iterative algorithm can generate more accurate parameter estimates than the least squares iterative algorithm.
Keywords:
Data filtering
Least squares
Iterative algorithm
Model decomposition
Nonlinear system
Journal
IF:
6
Papers:
1.4W
Citations:
4.1W
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