Frequency-domain subspace system identification using non-parametric noise models
From MaRDI portal
Publication:1614389
DOI10.1016/S0005-1098(02)00036-5zbMath1008.93066MaRDI QIDQ1614389
Publication date: 5 September 2002
Published in: Automatica (Search for Journal in Brave)
system identificationconsistencyasymptotic normalitysample covariance matrixfrequency-domain subspace algorithm
Frequency-response methods in control theory (93C80) Sampled-data control/observation systems (93C57) Identification in stochastic control theory (93E12)
Related Items (7)
A combined invariant-subspace and subspace identification method for continuous-time state-space models using slowly sampled multi-sine-wave data ⋮ Robust data driven \(H_\infty\) control for wind turbine ⋮ Two nonlinear optimization methods for black box identification compared ⋮ Hierarchical gradient-based identification of multivariable discrete-time systems ⋮ Identification of nonlinear systems using polynomial nonlinear state space models ⋮ An insight into instrumental variable frequency-domain subspace identification ⋮ Identification of EIV models by compensated PEM
Cites Work
- Continuous-time frequency domain subspace system identification
- Fast projection methods for minimal design problems in linear system theory
- Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
- Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets
- Analyses, Development, and Applications of TLS Algorithms in Frequency Domain System Identification
- Frequency domain system identification with missing data
- Best conditioned parametric identification of transfer function models in the frequency domain
- Subspace-based multivariable system identification from frequency response data
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Frequency-domain subspace system identification using non-parametric noise models
