Optimal Non-Asymptotic Rates for Quadratic Prediction Error Method in Time-Varying Parametric Predictor Models
The quadratic prediction error method, also known as nonlinear least squares, can achieve optimal non-asymptotic rates of convergence for a wide range of time-varying parametric predictor models satisfying certain identifiability conditions.
Finite-Sample Guarantees for Frequency Domain System Identification
Under sub-Gaussian colored noise and stability assumptions, the ETFE estimates are concentrated around the true frequency response values, with an error rate of O((du + √dudy)√M/Ntot), where Ntot is the total number of samples, M is the number of desired frequencies, and du, dy are the dimensions of the input and output signals.
Direct System Identification of Dynamical Networks with Partial Measurements: Maximum Likelihood Approach
The author proposes a direct approach based on maximum likelihood estimation to identify dynamic networks with missing data, transforming the problem into a more tractable form by leveraging knowledge about network interconnections.
Linear and Nonlinear System Identification with Regularization via L-BFGS-B Algorithm
The author proposes a method for identifying linear and nonlinear state-space models using the L-BFGS-B algorithm, showcasing improved results over classical methods and applicability to a broad range of models.
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