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On asymptotic optimality of cross-validation estimators for kernel-based regularized system identification
Kernel-based regularized system identification is one of the major advances in system
identification in the past decade. A recent focus is to develop its asymptotic theory and it has …
identification in the past decade. A recent focus is to develop its asymptotic theory and it has …
A Key Conditional Quotient Filter for Nonlinear, non-Gaussian and non-Markovian System
Y Zhao, F Wu, L Zhu - arxiv preprint arxiv:2501.05162, 2025 - arxiv.org
This paper proposes a novel and efficient key conditional quotient filter (KCQF) for the
estimation of state in the nonlinear system which can be either Gaussian or non-Gaussian …
estimation of state in the nonlinear system which can be either Gaussian or non-Gaussian …
Emergent Structure in Multi-agent Systems Using Geometric Embeddings
This work investigates the self-organization of multi-agent systems into closed trajectories, a
common requirement in unmanned aerial vehicle (UAV) surveillance tasks. In such …
common requirement in unmanned aerial vehicle (UAV) surveillance tasks. In such …
Input design for regularized system identification: Stationary conditions and sphere preserving algorithm
This article studies input design of kernel-based regularization methods for linear dynamical
systems, which has been formulated as a nonconvex optimization problem with the criterion …
systems, which has been formulated as a nonconvex optimization problem with the criterion …
Maximum likelihood and Bayesian optimization based AFD identification algorithms
Y Wang, W Mi - 2024 14th Asian Control Conference (ASCC), 2024 - ieeexplore.ieee.org
The adaptive Fourier decomposition (AFD) has been extensively researched within the
domain of system identification, noted for its ability to expedite convergence through the …
domain of system identification, noted for its ability to expedite convergence through the …
Application of Pseudorandom Maximum Length Binary Signals to Nonlinear Kernel-Based Estimation
This paper considers the identification of nonlinear systems using kernel-based estimation.
Recent literature has presented interesting results employing a linear kernel and a nonlinear …
Recent literature has presented interesting results employing a linear kernel and a nonlinear …