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Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering

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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.

Keywords: pseudo linear; multivariate pseudo; using data; least squares; identification methods; methods multivariate

Journal Title: Multidimensional Systems and Signal Processing
Year Published: 2018

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