.png){kind=icon}
.png){kind=icon}
Return
Auxiliary model-based maximum likelihood multi-innovation recursive least squares identification for multiple-input multiple-output systems☆
.png){kind=icon}
PRE
AI
H
Q
X
DOI：10.1016/j.jfranklin.2024.107352.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The aim of this paper is to propose novel identification methods for multiple-input multiple- output systems. Through decomposing a system into subsystems, the system identification model is derived. Based on the obtained sub-model, an auxiliary model-based maximum likelihood recursive least squares algorithm is derived for parameter estimation. For further enhancing the estimation accuracy, the auxiliary model-based maximum likelihood multi- innovation recursive least squares (AM-ML-MIRLS) algorithm is proposed based on the proposed algorithm. Simulation results test the proposed algorithms are all effective, and prove that the proposed AM-ML-MIRLS algorithm has the superior performances in capturing the dynamic properties of the system.
Keywords:
Multivariable system
Recursive identification
Maximum likelihood
Parameter estimation
Multi-innovation identification
Journal
J
IF:
3.7
Papers:
6.2K
Citations:
1.5W
Organization
No organization information available
Cited Papers