.png){kind=icon}
Return
Decomposition optimization method for switching models using EM algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s11071-023-08302-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study proposes a decomposition optimization-based expectation maximization algorithm for switching models. The identities of each sub-model are estimated in the expectation step, while the parameters are updated using the decomposition optimization method in the maximization step. Compared with the traditional expectation maximization algorithm and the gradient descent expectation maximization algorithm, the decomposition optimization-based expectation maximization algorithm avoids the matrix inversion and eigenvalue calculation; thus, it can be extended to complex nonlinear models and large-scale models. Convergence analysis and simulation examples are given to show the effectiveness of the proposed algorithm.
Keywords:
Parameter estimation
Switching model
Expectation maximization algorithm
Decomposition optimization method
Least squares algorithm
Journal
IF:
6
Papers:
1.4W
Citations:
4.1W
Organization
Cited Papers
ShareSaveShareSaveShareSaveVariational Bayesian approach for ARX systems with missing observations and varying time-delays.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveModel structure selection for switched NARX system identification: A randomized approachAUTOMATICA
OAAI
AUTOMATICAIF5.9
OAAIShareSaveShareSave
View more