Lecture Notes

LEC # TOPICS
1 Review of Linear Systems, Review of Stochastic Processes, Defining a General Framework ( PDF )
2 Review of Linear Systems, Review of Stochastic Processes, Defining a General Framework (cont.)
3 Introductory Examples for System Identification ( PDF )
4 Introductory Examples for System Identification (cont.)
5 Nonparametric Identification ( PDF )
6 Nonparametric Identification (cont.)
7 Input Design, Persistence of Excitation, Pseudo-random Sequences ( PDF )
8 Input Design, Persistence of Excitation, Pseudo-random Sequences (cont.)
9 Least Squares, Statistical Properties ( PDF )
10 Least Squares, Statistical Properties (cont.)
11 Parametrized Model Structures, One-step Predictor, Identifiability ( PDF )
12 Parametrized Model Structures, One-step Predictor, Identifiability (cont.)
13 Parameter Estimation Methods, Minimum Prediction Error Paradigm, Maximum Likelihood ( PDF )
14 Parameter Estimation Methods, Minimum Prediction Error Paradigm, Maximum Likelihood (cont.)
15 Convergence and Consistency, Informative Data, Convergence to the True Parameters ( PDF )
16 Convergence and Consistency, Informative Data, Convergence to the True parameters (cont.)
17 Asymptotic Distribution of PEM ( PDF )
18 Asymptotic Distribution of PEM (cont.)
19 Instrumental Variable Methods, Identification in Closed Loop, Asymptotic Results ( PDF )
20 Instrumental Variable Methods, Identification in Closed Loop, Asymptotic Results (cont.)
21 Computation, Levinson Algorithm, Recursive Estimation ( PDF )
22 Computation, Levinson Algorithm, Recursive Estimation (cont.)
23 Identification in Practice, Error Filtering, Order Estimation, Model Structure Validation, Examples ( PDF - 1.7 MB )
24 Identification in Practice, Error Filtering, Order Estimation, Model Structure Validation, Examples (cont.)

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