  As far as the identification of linear time-invariant state-space
representation is concerned, among all of the solutions available in the
literature, the subspace-based state-space model identification techniques have
proved their efficiency in many practical cases since the beginning of the
90's. This paper introduces an overview of these techniques by focusing on
their formulation as a least-squares problem. Apart from an article written by
J. Qin, to the author's knowledge, such a regression formulation is not totally
investigated in the books which can be considered as the references as far as
subspace-based identification is concerned. Thus, in this paper, a specific
attention is payed to the regression-based techniques used to identify systems
working under open-loop as well as closed-loop conditions.
