Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

Liu, Qinyao and Ding, Feng and Yang, Erfu (2018) Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems. Digital Signal Processing: A Review Journal, 83. pp. 323-331. ISSN 1051-2004 (https://doi.org/10.1016/j.dsp.2018.09.010)

[thumbnail of Liu-etal-DSP-2018-Parameter-estimation-algorithm-for-multivariable-controlled-autoregressive-autoregressive]
Preview
 Text. Filename: Liu_etal_DSP_2018_Parameter_estimation_algorithm_for_multivariable_controlled_autoregressive_autoregressive.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (266kB)| Preview

Abstract

This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm.

CORE (COnnecting REpositories)