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• W4246238229 abstract "This chapter deals with the problem of estimating an unknown parameter in a given dynamical model describing a real system. Such a problem is very frequently encountered in a huge variety of applications. The chapter investigates how to resort to the observation of the measurable external variables affecting the systems to find the value for the missing parameter. To estimate the unknown parameter, a possibility is to resort to Kalman filtering: The basic idea is to deal with parameter θ as a new state variable. The state estimation problem calls therefore for a nonlinear estimation method such as the extended Kalman filter (EKF). To set up the EKF technique, main issues are tuning the variance of the fake noise and choosing the initialization of the Riccati equation. A number of variants on the EKF technique have been studied to ensure better convergence properties." @default.
• W4246238229 created "2022-05-12" @default.
• W4246238229 date "2019-03-25" @default.
• W4246238229 modified "2023-09-24" @default.
• W4246238229 title "Parameter Identification in a Given Model" @default.
• W4246238229 doi "https://doi.org/10.1002/9781119546405.ch10" @default.
• W4246238229 hasPublicationYear "2019" @default.
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