Abstract
Recently severed subspace methods have appeared in the literature for multivariable discrete-time state space identification, where state space models are computed directly from input/output data. These state space identification methods are viewed as the better alternatives to polynomial model identification, owing to the better numerical conditioning associated with state space models, especially for high-order multivariable systems.
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