| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 11032462 | Digital Signal Processing | 2018 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Qinyao Liu, Feng Ding, Erfu Yang,