Lecture Notes

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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.)