520a Simpca with Modified Instrumental Variable to Improve Estimation Accuracy

Jin Wang and S. Joe Qin Based on projection techniques in Euclidean space, subspace identification methods (SIMs) have been one of the main streams of research in system identification (Gevers, 2003). Several representative algorithms have been published, including canonical variate analysis (CVA, Larimore, 1983; 1990), numerical algorithm of subspace state space system identification (N4SID, Van Overschee and De Moor, 1994) and multivariate output-error state space (MOESP, Verhaegen, 1994). The asymptotic properties of these subspace algorithms also have been investigated in the past decade and consistency conditions of the estimates have been identified (Deistler et al., 1995; Peternell et al., 1996; Jansson and Wahlberg, 1998; Bauer et al., 1999; Bauer and Jansson, 2000; Knudsen, 2001). The effect of weighting matrices and more explicit expressions for the asymptotic variance of the model estimates have been obtained recently (Bauer and Ljung, 2002; Gustafsson, 2002).