Input selection in ARX model estimation using group lasso regularization
Input selection in ARX model estimation using group lasso regularization is a scholarly work, published in 2018 in ''IFAC Proceedings Volumes''. The main subjects of the publication include selection (genetic algorithm), estimation theory, model selection, regularization, Structural health monitoring, extension, computer science, identification, least-squares function approximation, system identification, mathematical optimization, statistics, Lasso, fault detection and isolation, project management estimation, mathematics, and algorithm. The authors investigate an input selection extension in least-squares ARX estimation and show that better model estimates are achieved compared to the least-square ssolution, in particular, for short batches of estimation data.