Gradient-based iterative identification methods for multivariate pseudo-linear moving average systems using the data filtering

Abstract

This paper studies the parameter identification problems of multivariate pseudo-linear moving average systems. By means of the data filtering technique, a multivariate pseudo-linear moving average system is transformed into two identification models, and a filtering-based gradient iterative algorithm is presented for estimating the parameters of these two identification models interactively. The analysis indicates that the proposed filtering-based gradient iterative algorithm can achieve a higher computational efficiency than the gradient-based iterative algorithm, and the numerical simulation results demonstrate that the proposed methods are effective.