N2SID: nuclear norm subspace identification of innovation models

DOI10.1016/j.automatica.2016.05.021zbMath1344.93035arXiv1501.04495OpenAlexW2497631405MaRDI QIDQ311894

Mohammad Hasan, M. Dambrine

Publication date: 13 September 2016

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1501.04495

zbMATH Keywords

optimizationstructural constraintsinnovation state space modelssubspace system identification

Mathematics Subject Classification ID

Convex programming (90C25) System identification (93B30) Multivariable systems, multidimensional control systems (93C35) Discrete-time control/observation systems (93C55) Identification in stochastic control theory (93E12)

Related Items (7)

A linear algorithm for the minimal realization problem in physical coordinates with a non-invertible output matrix ⋮ Nuclear norm-based recursive subspace prediction of time-varying continuous-time stochastic systems via distribution theory ⋮ N2SID: nuclear norm subspace identification of innovation models ⋮ Maximum entropy vector kernels for MIMO system identification ⋮ Direct synthesis signal sets for multi-input system identification ⋮ Subspace-based spectrum estimation in innovation models by mixed norm minimization ⋮ Continuous-time Laguerre-based subspace identification utilising nuclear norm minimisation

Uses Software

• System Identification Toolbox
• Daisy

Cites Work

• N2SID: nuclear norm subspace identification of innovation models
• On consistency of subspace methods for system identification
• Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
• 4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
• Nuclear norm system identification with missing inputs and outputs
• Hankel Matrix Rank Minimization with Applications to System Identification and Realization
• Frequency Domain Subspace Identification Using Nuclear Norm Minimization and Hankel Matrix Realizations
• Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
• Filtering and System Identification

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