.png){kind=icon}
Return
A Comprehensive Expectation Identification Framework for Multirate Time-Delayed Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2022.3194656.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The expectation maximization (EM) algorithm has been extensively used to solve system identification problems with hidden variables. It needs to calculate a derivative equation and perform a matrix inversion in the EM-M step. The equations related to the EM algorithm may be unsolvable for some complex nonlinear systems, and the matrix inversion has heavy computational costs for large-scale systems. This article provides two expectation-based algorithms with the aim of constructing a comprehensive expectation framework concerning different kinds of time-delayed systems: 1) for a small-scale linear system, the classical EM algorithm can quickly obtain the parameter and time-delay estimates; 2) for a complex nonlinear system with low order, the proposed expectation gradient descent algorithm can avoid derivative function calculation; 3) for a large-scale system, the proposed expectation multidirection algorithm does not require eigenvalue calculation and has less computational costs. These two algorithms are developed based on the gradient descent and multidirection methods. Under such an expectation framework, different kinds of models are identified on a case-by-case basis. The convergence analysis and simulation examples show the effectiveness of the algorithms.
Keywords:
Mathematical models
Eigenvalues and eigenfunctions
Computational modeling
Convergence
Computational efficiency
Nonlinear systems
Large-scale systems
Comprehensive expectation framework
gradient descent (GD) method
maximization method
multidirection (MD) method
time-delayed system
Journal
IF:
9.9
Papers:
8.6K
Citations:
6.0W
Organization
Cited Papers
ShareSaveVariational Bayesian approach for ARX systems with missing observations and varying time-delays.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSave
ShareSaveShareSaveSystem identification in a networked environment using second order statistical propertiesAUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveShareSave
View more