TY  - CHAP
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6161172&isnumber=6159299
TI  - Set-membership identification of Hammerstein-Wiener systems
AV  - public
KW  - Decision support systems; Noise; Optimization; Polynomials; Uncertain systems; Uncertainty; Vector
Y1  - 2011/12//
SP  - 2819
N2  - Set-membership identification of Hammerstein-Wiener models is addressed in the paper. First, it is shown that computation of tight parameter bounds requires the solutions to a number of nonconvex constrained polynomial optimization problems where the number of decision variables increases with the length of the experimental data sequence. Then, a suitable convex relaxation procedure is presented to significantly reduce the computational burden of the identification problem. A detailed discussion of the identification algorithm properties is reported. Finally, a simulated example is used to show the effectiveness and the computational tractability of the proposed approach.
A1  - Cerone, Vito
A1  - Piga, Dario
A1  - Regruto, Diego
PB  - IEEE
SN  - 978-1-61284-799-3
T2  - Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC)
EP  - 2824
ID  - eprints2445
N1  - 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), held in Orlando USA), December 12-15, 2011
ER  -