Continuous-time approaches to system indentification—a survey
Automatica (Journal of IFAC) - Identification and system parameter estimation
Automatica (Journal of IFAC)
On Tikhonov regularization, bias and variance in nonlinear system identification
Automatica (Journal of IFAC)
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Journal of the ACM (JACM)
Markov chain Monte Carlo methods with applications to signal processing
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Signal Processing
The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer
IEEE Transactions on Computers
Parameter estimation for continuous-time models-A survey
Automatica (Journal of IFAC)
An instrumental variable method for real-time identification of a noisy process
Automatica (Journal of IFAC)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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This paper presents an analysis of some regularization aspects in continuous-time model identification. The study particulary focuses on linear filter methods and shows that filtering the data before estimating their derivatives corresponds to a regularized signal derivative estimation by minimizing a compound criterion whose expression is given explicitly. A new structure based on a null phase filter corresponding to a true regularization filter is proposed and allows to discuss the filter phase effects on parameter estimation by comparing its performances with those of the Poisson filter-based methods. Based on this analysis, a formulation of continuous-time model identification as a joint system input-output signal and model parameter estimation is suggested. In this framework, two linear filter methods are interpreted and a compound criterion is proposed in which the regularization is ensured by a model fitting measure, resulting in a new regularization filter structure for signal estimation.