TY  - JOUR
EP  -  1142
SN  - 0167-6911
N2  - Abstract The problem of identifying a fixed-order {FIR} approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order {FIR} model providing the best approximation of the input?output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example. 
PB  - Elsevier
TI  - Fixed-order FIR approximation of linear systems from quantized input and output data 
IS  - 12
AV  - public
Y1  - 2013/12//
ID  - eprints2472
KW  - FIR models; Linear programming; Quantized identification; Robust optimization
JF  - Systems & Control Letters
SP  - 1136 
A1  - Cerone, Vito
A1  - Piga, Dario
A1  - Regruto, Diego
VL  - 62
UR  - http://www.sciencedirect.com/science/article/pii/S0167691113001990
ER  -