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Maximum Likelihood Recursive Identification for the Multivariate Equation-Error Autoregressive Moving Average Systems Using the Data Filtering
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DOI：10.1109/ACCESS.2019.2905576.png)
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Abstract
En 中文
The maximum likelihood principle has wide applications in system identification. This paper studies the maximum likelihood identification problems of the multivariate equation-error systems with colored noise. The system is broken down into several subsystems based on the number of the outputs. The key is to transform the subsystem into a controlled autoregressive moving average model and a noise model. Based on the maximum likelihood principle and the data filtering technique, a filtering-based maximum likelihood recursive generalized extended least squares algorithm is presented for estimating the parameters of these two models. For comparison, a maximum likelihood recursive generalized extended least squares algorithm is presented. Finally, the simulation example results confirm the effectiveness of the two algorithms.
Keywords:
Parameter estimation
maximum likelihood
data filtering
multivariate system
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