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A system-input-state joint estimation algorithm with multi-scale Bayesian optimization
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DOI：10.1016/j.ymssp.2026.113902.png)
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Abstract
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• A joint system-input-state identification algorithm with Bayesian optimization is proposed. • A novel optimization function is presented to describe time–frequency domain information. • Optimization function can describe the accuracy of the model at multi time scales and steps. • Numerical simulations verify effectiveness, adaptability, and robustness of the algorithm. • The proposed algorithm is applied to the 632 m high Shanghai Tower.
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8.9
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1.3W
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7.4W
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