Nuclear norm system identification with missing inputs and outputs

DOI10.1016/j.sysconle.2013.04.005zbMath1279.93040OpenAlexW2120142311MaRDI QIDQ2434436

Yanyan Li

Publication date: 5 February 2014

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-96704

zbMATH Keywords

system identificationnuclear normsubspace methodlow-rank matrix approximationHankel structure

Mathematics Subject Classification ID

System identification (93B30) Control/observation systems with incomplete information (93C41)

Related Items

Fractional order neural networks for system identification ⋮ 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 likelihood estimation of linear SISO models subject to missing output data and missing input data ⋮ Maximum entropy vector kernels for MIMO system identification ⋮ On sparsity‐inducing methods in system identification and state estimation ⋮ The Nesterov accelerated gradient algorithm for auto-regressive exogenous models with random lost measurements: interpolation method and auxiliary model method ⋮ Subspace-based spectrum estimation in innovation models by mixed norm minimization ⋮ Recursive and iterative least squares parameter estimation algorithms for observability canonical state space systems ⋮ Unnamed Item ⋮ Flip-flop spectrum-revealing QR factorization and its applications to singular value decomposition ⋮ Efficient proximal mapping computation for low-rank inducing norms ⋮ Spectrum estimation with missing values: A regularized nuclear norm minimization approach ⋮ Continuous-time Laguerre-based subspace identification utilising nuclear norm minimisation

Uses Software

• Daisy

Cites Work

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