Abstract
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.
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