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Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems
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DOI：10.1007/s11071-013-0851-6.png)
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Abstract
En 中文
We consider the parameter estimation problem for Hammerstein finite impulse response (FIR) systems. An estimated noise transfer function is used to filter the input-output data of the Hammerstein system. By combining the key-term separation principle and the filtering theory, a recursive least squares algorithm and a filtering-based recursive least squares algorithm are presented. The proposed filtering-based recursive least squares algorithm can estimate the noise and system models. The given examples confirm that the proposed algorithm can generate more accurate parameter estimates and has a higher computational efficiency than the recursive least squares algorithm.
Keywords:
Parameter estimation
Recursive least squares
Filtering theory
Key-term separation principle
Hammerstein system