A study in continuous time of the identification of initial conditions and/or parameters of deterministic system by means of a Kalman-type filter
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Publication:1242831
DOI10.1016/0378-4754(77)90060-XzbMath0368.93032OpenAlexW1978512647MaRDI QIDQ1242831
Publication date: 1977
Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0378-4754(77)90060-x
System identification (93B30) Estimation and detection in stochastic control theory (93E10) Linear systems in control theory (93C05) Control/observation systems governed by ordinary differential equations (93C15)
Related Items (8)
The identification of the parameters of time-invariant stochastic systems by a method derived from the continuous-time Kalman filter ⋮ The analysis of a parameter identification algorithm which was derived from the continuous time Kalman filter ⋮ The observability of initial- and lagging-state observers ⋮ The relationship between a continuous-time identification algorithm based on the deterministic filter and least-squares methods ⋮ Determination of the unknown Cauchy data in a linear parabolic problem from the measured data at the final time ⋮ A study of the Kalman filter as a state estimator of deterministic and stochastic systems ⋮ A Kalman filter type of extension to a deterministic gradient technique for parameter estimation ⋮ The Robustness ot the Fixed Point Smoothing Algorithm
Cites Work
- Unnamed Item
- Use of an analogue computer in the application of Kalman filter methods of system identification in the presence of noise
- Comparison of six one-line identification algorithms
- Techniques for initial condition estimation in linear systems†
- The Luenberger canonical form in the state/parameter estimation of linear systems
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