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Multiple-model state-space system identification with time delay using the EM algorithm
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AI
DOI：10.1016/j.jfranklin.2024.107113.png)
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Abstract
En 中文
For a dynamic process identification throughout the whole operating range under diverse operating conditions, it is difficult to capture the process dynamics by a single process model in which the traditional identification method can be adopted to implement parameter estimation. By using the multiple dual-rate state-space models to approach the parameter-varying time- delay systems with different operating conditions, this paper explores an EM algorithm to simultaneously estimate the hidden variable, the parameter vector, the state variable and the time-delay by introducing hidden variables and by using a Kalman smoother.
Keywords:
Time delay estimation
Parameter estimation
Expectation-maximization algorithm
Kalman smoother
Journal
J
IF:
3.7
Papers:
6.3K
Citations:
1.5W