TY  - CHAP
N1  - 16th IFAC Symposium on System Identification held in Brussels (Belgium), 11-13 July 2012
AV  - none
SN  - 1474-6670
EP  - 1184
ID  - eprints2450
N2  - In this paper we consider 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. In particular, a FIR model of given order which provides 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. First we show that the considered problem can be formulated in terms of robust optimization. Then, we present two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques. The effectiveness of the proposed approach is illustrated by means of a simulation example.
SP  - 1179
TI  - FIR approximation of linear systems from quantized records
Y1  - 2012///
A1  - Cerone, Vito
A1  - Piga, Dario
A1  - Regruto, Diego
UR  - http://dx.doi.org/10.3182/20120711-3-BE-2027.00239
PB  - IFAC
T2  - 16th IFAC Symposium on System Identification
KW  - Bounded Error Identification
ER  -