TY  - JOUR
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6018992&isnumber=6139300
KW  - Errors-in-variables (EIV); linear matrix inequality (LMI) relaxation;  set-membership identification; sparse polynomial; optimization
TI  - Set-Membership Error-in-variables identification through convex relaxation techniques 
AV  - none
N2  - In this technical note, the set membership error-in-variables identification problem is considered, that is the identification of linear dynamic systems when both output and input measurements are corrupted by bounded noise. A new approach for the computation of parameter uncertainty intervals is presented. First, the identification problem is formulated in terms of nonconvex optimization. Then, relaxation techniques based on linear matrix inequalities are employed to evaluate parameter bounds by means of convex optimization. The inherent structured sparsity of the original identification problems is exploited to reduce the computational complexity of the relaxed problems. Finally, convergence properties and complexity of the proposed procedure are discussed. Advantages of the presented technique with respect to previously published results are discussed and shown by means of two simulated examples.
SN  - 0018-9286
EP  - 522
ID  - eprints2467
IS  - 2
JF  - IEEE Transactions on Automatic Control 
Y1  - 2012/02//
SP  - 517
PB  - IEEE 
A1  - Cerone, Vito
A1  - Piga, Dario
A1  - Regruto, Diego
VL  - 57
ER  -