Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems
By Meihang Li, Ximei Liu. International Journal of Adaptive Control and Signal Processing, 2020
191 citations recorded by the source corpus.
For a dual‐rate sampled‐data stochastic system with additive colored noise, a dual‐rate identification model is obtained by using the polynomial transformation technique, which is suitable for the available dual‐rate measurement data. Based on the obtained model, a maximum likelihood least squares‐based iterative (ML‐LSI) algorithm is presented for identifying the parameters of the dual‐rate sampled‐data stochastic system. In order to improve the computation efficiency of the algorithm, the identification model of a dual‐rate sampled‐data stochastic system is divided into two subidentification models with smaller dimensions and fewer parameters, and a maximum likelihood hierarchical least squares‐based iterative (H‐ML‐LSI) algorithm is proposed for these subidentification models by using the hierarchical identification principle. The simulation results indicate that the proposed algorithms are effective for identifying dual‐rate sampled‐data stochastic systems and the H‐ML‐LSI algorithm has a higher computation efficiency than the ML‐LSI algorithm.
Subfields: Artificial Intelligence, Civil and Structural Engineering, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/42eb1f9a27e8b235c92cfebf0278687ea4d0cdb8
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Citations in the corpus
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