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Medientyp:
Buch
Titel:
Recursive estimation and time-series analysis
:
an introduction for the student and practitioner
Enthält:
1. IntroductionPt. I. Recursive estimation of parameters in linear regression models -- 2. Recursive estimation: a simple tutorial introduction -- 3. Recursive least squares estimation -- 4. Recursive estimation of time variable parameters in regression models -- 5. Unobserved component models -- Pt. II. Recursive estimation of parameters in transfer function models -- 6. Transfer function models and the limitations of recursive least squares -- 7. Optimal identification and estimation of discrete-time transfer function models -- 8. Optimal identification and estimation of continuous-time transfer function models -- 9. Identification of transfer function models in closed-loop -- 10. Real-time recursive parameter estimation -- Pt. III. Other topics -- 11. State-dependent parameter (SDP) estimation -- 12. Data-based mechanistic (DBM) modelling -- Epilogue -- A. The K.F. Gauss derivation of recursive least squares -- B. Basic mathematical and statistical background -- C. Stochastic approximation -- D. Deterministic regularization and stochastic fixed interval smoothing -- E. The instantaneous cost function associated with the recursive least squares algorithm -- F. Maximum likelihood derivation of the refined instrumental variable algorithm -- G. The CAPTAIN toolbox for Matlab: an overview.
1. Introduction -- Pt. I. Recursive estimation of parameters in linear regression models -- 2. Recursive estimation: a simple tutorial introduction -- 3. Recursive least squares estimation -- 4. Recursive estimation of time variable parameters in regression models -- 5. Unobserved component models -- Pt. II. Recursive estimation of parameters in transfer function models -- 6. Transfer function models and the limitations of recursive least squares -- 7. Optimal identification and estimation of discrete-time transfer function models -- 8. Optimal identification and estimation of continuous-time transfer function models -- 9. Identification of transfer function models in closed-loop -- 10. Real-time recursive parameter estimation -- Pt. III. Other topics -- 11. State-dependent parameter (SDP) estimation -- 12. Data-based mechanistic (DBM) modelling -- Epilogue -- A. The K.F. Gauss derivation of recursive least squares -- B. Basic mathematical and statistical background -- C. Stochastic approximation -- D. Deterministic regularization and stochastic fixed interval smoothing -- E. The instantaneous cost function associated with the recursive least squares algorithm -- F. Maximum likelihood derivation of the refined instrumental variable algorithm -- G. The CAPTAIN toolbox for Matlab: an overview.