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- W4310971543 abstract "In this paper, the parametric identification is addressed by a kernel-based model with covariance and a novel model order selection algorithm. The kernel-based model is uti-lized for training the sampled frequency response characteristics, which is insufficient for parametric identification because of noisy and discrete data. The kernel-based frequency response model improves the parametric identification by using the high covariance data. In addition, prior knowledge of the model order is essential for parametric identification. This paper proposes a novel model order selection based on the robust stability criterion of disturbance observer (DOB). The effectiveness of the proposed algorithm is verified through numerical simulations under several conditions." @default.
- W4310971543 created "2022-12-21" @default.
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- W4310971543 date "2022-10-17" @default.
- W4310971543 modified "2023-10-18" @default.
- W4310971543 title "Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability" @default.
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- W4310971543 doi "https://doi.org/10.1109/iecon49645.2022.9968765" @default.
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