摘要：AbstractWe discuss the linear system identification methods that are based on a regularized estimation problem including a rank penalty (typically formulated in terms of nuclear norm).We provide a common framework, under which most of these procedures can be recast. Following the Bayesian approach to system identification, we also introduce a Gaussian prior inducing a rank penalty and we prove the effectiveness of this method through a Monte-Carlo experiment.
关键词：KeywordsIdentificationLearningNumerical MethodsLinear Systems
