Gradient-based parameter identification algorithms for observer canonical state space systems using state estimates
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Publication:736869
DOI10.1007/s00034-014-9911-5zbMath1341.93091OpenAlexW1980021567MaRDI QIDQ736869
Publication date: 5 August 2016
Published in: Circuits, Systems, and Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00034-014-9911-5
identificationKalman filterstate space modelnumerical algorithmmulti-innovation identification theorystochastic gradient
Filtering in stochastic control theory (93E11) Stochastic programming (90C15) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)
Related Items (8)
A multi-innovation recursive least squares algorithm with a forgetting factor for Hammerstein CAR systems with backlash ⋮ Joint estimation of states and parameters for an input nonlinear state-space system with colored noise using the filtering technique ⋮ Combined parameter and state estimation algorithms for multivariable nonlinear systems using MIMO Wiener models ⋮ Kalman filtering based gradient estimation algorithms for observer canonical state-space systems with moving average noises ⋮ Model equivalence-based identification algorithm for equation-error systems with colored noise ⋮ Recursive and iterative least squares parameter estimation algorithms for observability canonical state space systems ⋮ Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition ⋮ A robust meta-heuristic adaptive Bi-CGSTAB algorithm to online estimation of a three DoF state–space model in the presence of disturbance and uncertainty
Uses Software
Cites Work
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