Abstract
This paper considers the parameter estimation problem of stochastic systems (i.e., controlled autoregressive systems) by adopting the projection method and the hierarchical identification principle. To improve the performance of the projection identification algorithm, a generalized projection algorithm is proposed by introducing a data window length. By means of the hierarchical identification principle, we divide the system into two fictitious subsystems and derive a two-stage generalized projection identification algorithm. The simulation example tests the effectiveness of the proposed identification algorithms.
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Acknowledgements
This work was supported by the National Key R&D Program of China (No. 2016YFE0202200) and the Research Foundation of China University of Petroleum-Beijing At Karamay (No. CYJ2017B-01-001).
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Hu, Y., Zhou, Q., Yu, H. et al. Two-Stage Generalized Projection Identification Algorithms for Stochastic Systems. Circuits Syst Signal Process 38, 2846–2862 (2019). https://doi.org/10.1007/s00034-018-0996-0
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DOI: https://doi.org/10.1007/s00034-018-0996-0