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.
Original language | English |
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Pages (from-to) | 323-331 |
Number of pages | 9 |
Journal | Digital Signal Processing: A Review Journal |
Volume | 83 |
Early online date | 25 Sept 2018 |
DOIs | |
Publication status | Published - 31 Dec 2018 |
Keywords
- auxiliary model
- coupling identification
- multivariable system
- parameter estimation
- stochastic gradient