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This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior;
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English
English [CC]
FREE
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Description
time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.
Course content
- Review of Linear Systems, Review of Stochastic Processes, Defining a General Framework Unlimited
- Introductory Examples for System Identification Unlimited
- Nonparametric Identification Unlimited
- Least Squares, Statistical Properties Unlimited
- Parameter Estimation Methods, Minimum Prediction Error Paradigm, Maximum Likelihood Unlimited
- Convergence and Consistency, Informative Data, Convergence to the True Parameters Unlimited
- Asymptotic Distribution of PEM Unlimited
- Instrumental Variable Methods, Identification in Closed Loop, Asymptotic Results Unlimited
- Computation, Levinson Algorithm, Recursive Estimation Unlimited
- Identification in Practice, Error Filtering, Order Estimation, Model Structure Validation, Examples Unlimited
Instructor
Massachusetts Institute of Technology
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