Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration

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Abstract

The Newton iteration is basic for solving nonlinear optimization problems and studying parameter estimation algorithms. In this letter, a maximum likelihood estimation algorithm is developed for estimating the parameters of Hammerstein nonlinear controlled autoregressive autoregressive moving average (CARARMA) systems by using the Newton iteration. A simulation example is provided to show the effectiveness of the proposed algorithm.

Keywords

Iterative identification
Hammerstein models
Maximum likelihood
Newton iteration

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This work was supported by the 111 Project (B12018), the National Natural Science Foundation of China (60975064), the Natural Science Foundation for Colleges and Universities of Jiangsu Province (10KJD120002, 11KJD510004), and the Natural Science Project of Nantong University (10ZJ007).