Title :
Learning models from data: the set membership approach
Author_Institution :
Dipt. di Autom. e Inf., Politecnico di Torino, Italy
Abstract :
The problem of identifying complex linear systems from noise corrupted data is investigated, considering that only approximate models can be estimated and the effects of unmodeled dynamics have to be accounted for. The paper presents a unified view of the set membership identification theory, as recently evolved by the author et al. (1997), aiming to deliver not only a model of the system to be identified, but also a measure of its approximation. Optimality and convergence results are reported, related to identification problems for different settings of experimental conditions and noise assumptions
Keywords :
approximation theory; convergence of numerical methods; identification; large-scale systems; linear systems; optimisation; set theory; approximation; complex linear systems; convergence; identification; learning models; noise corrupted data; optimisation; set membership; set theory; unmodeled dynamics; Control design; Convergence; Decision making; Fault detection; Fault diagnosis; Filtering; Linear systems; Noise measurement; Samarium; Smoothing methods;
Conference_Titel :
American Control Conference, 1998. Proceedings of the 1998
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-4530-4
DOI :
10.1109/ACC.1998.694653
