2000 character limit reached
Recursive identification with regularization and on-line hyperparameters estimation
Published 29 Dec 2023 in stat.ME, cs.SY, eess.SY, and math.OC | (2401.00097v3)
Abstract: This paper presents a regularized recursive identification algorithm with simultaneous on-line estimation of both the model parameters and the algorithms hyperparameters. A new kernel is proposed to facilitate the algorithm development. The performance of this novel scheme is compared with that of the recursive least squares algorithm in simulation.
- Akaïke, H. (1979). Smoothness priors and the distributed lag estimators. Technical report Department of statistics.
- Maximum entropy kernels for system identification. Proc. IFAC symposium on Self-Adaptive Systems, Teddinghton.
- Carli, F.P. (2014). On the maximum entropy property of the first-order stable spline kernel and its implications. In 2014 IEEE Conference on Control Applications (CCA), 409–414. 10.1109/CCA.2014.6981380.
- Maximum entropy kernels for system identification. IEEE Transactions on Automatic Control, 62(3), 1471–1477. 10.1109/TAC.2016.2582642.
- On the estimation of transfer functions, regularizations and gaussian processes revisited. Automatica, 48(8), 1525–1535.
- Adaptive system identification with regularized fir models. Proc. 14th IFAC Workshop on adaptive and learning control systems.
- Adaptive control. Springer, London, 2nd edition.
- Leamer, E. (1972). A class of informative priors and distributed lag analysis. Econometrica, 40(6), 1059–1081.
- Theory and practice of recursive identification. MIT Press, Cambridge.
- A new kernel approach for linear system identification. Automatica, 46(1), 81–93.
- Regularized System Identification: Learning Dynamic Models from Data. Communications and Control Engineering. Springer International Publishing, Cham. 10.1007/978-3-030-95860-2.
- Kernel methods in system identification, machine learning and function estimation: A survey. Automatica, 50(3), 657–682. 10.1016/j.automatica.2014.01.001.
- Online identification of time-varying systems: A bayesian approach. Proc. 55th Conference on Decision and Control (CDC).
- Online identification system identification. European Control Conference.
- Closed-loop output error identification algorithms with predictors based on generalized orthonormal transfer functions: Convergence conditions and bias distribution. Automatica, 125.
- Whitacker, H. (1959). Design of a model-reference adaptive control system for aircraft. In Report R-164. Instrumentation Lab MIT, Boston.
- Young, P. (2011). Recursive and time series Analysis, An introduction for the student and practitionner. Springer, London.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.