Parameter estimation in stochastic grey-box models
By Niels Rode Kristensen, Henrik Madsen, Sten Bay Jørgensen. at - Automatisierungstechnik, 2004
399 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/53b871f03648249aa4dd92b3864bd64d9215dabc
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In Electrical and Electronic Engineering
In Management Science and Operations Research
Citations in the corpus
- Estimation of non-linear continuous time models for the heat exchange dynamics of building integrated photovoltaic modules (2008, methodology): “For a detailed outline of the method is we refer to [11,12], or [10].”
- Identifying suitable models for the heat dynamics of buildings (2011, neutral): “Since the conditional densities are Gaussian a Kalman filter can be used to calculate the likelihood function, and an optimization algorithm can be applied to maximize it, thereby calculating the maximum likelihoodestimates(see[7]foradetaileddiscussion).”
- System identification of nonlinear state-space models (2011, methodology): “Many approaches using various suboptimal nonlinear filters (such as the extended Kalman filter) to approximate the cost Jacobian have been proposed [5] [22] [27].”