An active learning algorithm for linear systems attains the minimal sample complexity for accurate identification using ordinary least squares and semidefinite programming with centered excitation.
Near optimal finit e time identification of arbitrary linear dynamical systems
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Optimal Centered Active Excitation in Linear System Identification
An active learning algorithm for linear systems attains the minimal sample complexity for accurate identification using ordinary least squares and semidefinite programming with centered excitation.