Maximum Entropy Vector Kernels for MIMO system identification
TL;DR: In this paper, the structure of the Hankel matrix has been exploited to control the complexity of linear system identification, measured by the McMillan degree, stability and smoothness of the identified models.
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About: This article is published in Automatica. The article was published on 01 May 2017. and is currently open access. The article focuses on the topics: Hankel matrix & Matrix norm.
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Sparse plus Low rank Network Identification: A Nonparametric Approach
Mattia Zorzi,Alessandro Chiuso +1 more
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Maximum entropy properties of discrete-time first-order stable spline kernel
Tianshi Chen,Tohid Ardeshiri,Francesca Paola Carli,Alessandro Chiuso,Lennart Ljung,Gianluigi Pillonetto +5 more
TL;DR: In this article, the authors formulate the exact maximum entropy problem solved by the first order stable spline (SS-1) kernel without Gaussian and uniform sampling assumptions and derive the special structure of the SS-1 kernel under general sampling assumption.