Recursive subspace identification of linear and non-linear Wiener state-space models

The problem of MIMO recursive identification is analyzed within the framework of subspace model identification (SMI) and the use of recent signal processing algorithms for the recursive update of the singular value decomposition (SVD) is proposed. To accommodate for arbitrary correlation of the disturbances, an instrumental variable (IV) approach is followed. In particular, recursive formulations for the subspace identification algorithms of the multivariable output-error state space (MOESP) class are given. A recursive algorithm for the identification of non-linear models of the Wiener type is also obtained.