Editorial

摘要

The emergence of subspace-based identification methods more than a decade ago has stimulated a lot of research and interest in the development, understanding and use of the new algorithms for the identification of both linear and nonlinear systems. The central goal of the first generation of subspace identification methods was the estimation or better the low-rank approximation of the column or row space of a matrix derived by linear projection from structured (block-Hankel) matrices filled with measurements taken from the system to be identified. This low-rank approximation is the key in deriving approximations of the system matrices of a state-space model.