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Sparse Learning in System Identification: Debiasing and Infinite-Dimensional Algorithms
(2021)In the traditional system identification techniques, a priori model structure is widely assumed to be available and the unknown parameters of the assumed model structure are estimated by maximizing the adherence of the assumed model structure to the experimental data. However, selecting the model structure can be problematic, sometimes leading to overfitting. Recent developments in the regularization based system identification methods, ...Master Thesis