This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique,… Click to show full abstract
This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective.
               
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