Abstract
This paper considers the identification problems of Box-Jenkins systems based on the data filtering technique and maximum likelihood principle. After using the noise polynomial to filter the input and output data, two identification models are obtained. Then a maximum likelihood stochastic gradient algorithm and a stochastic gradient estimation algorithm are derived to interactively estimate the parameters of the two identification models. The simulation results show that the proposed algorithms can effectively estimate the parameters of Box-Jenkins systems.
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