Course information
- Comple course name: 051588 - MODEL IDENTIFICATION AND DATA ANALYSIS - 1ST MODULE (BITTANTI SERGIO) {051587 - MODEL IDENTIFICATION AND DATA ANALYSIS [sezione A]}
- Profesor: Simone Garatti
- Tutor: Marco Raffaele Rapizza
- Accademic year:: 2021-2022
- Recordings:: link to recordings archive
- Webeep: link to webeep
Lessons
| Date | Recording | Whiteboard | Title | Professor's notes |
|---|---|---|---|---|
| 02 21 | Recording | Whiteboard | Introduction Stochastic processes (SP) |
1.1_MIDA_Introduction 1.2_MIDA_Stochastic_Processes |
| 02 22 | Recording | Whiteboard | Weak description of SP Stationary stochastic processes (SSP) White noise (WN) Moving average processes (MA) |
1.2_MIDA_Stochastic_Processes 1.3_MIDA_Model_Classes |
| 02 23 | Recording | Whiteboard | MA stationary? Covariance properties for SSP MA(inf) processes MA(inf) stationary? |
1.3_MIDA_Model_Classes |
| 02 28 | Recording | Whiteboard | Auto Regressive AR ARMA Steady state solutions Shift Operator Operational representation of ARMA Transfer function |
1.3_MIDA_Model_Classes |
| 03 01 | Recording | Whiteboard | Transfer function composition (series/parallel) Switch shift operator powers Zeros and poles Assintotically stable Minimum fase When ARMA is well-defined? |
1.3_MIDA_Model_Classes |
| 03 02 | Recording | Whiteboard | Solutions different from steady-state Computing ARMA weak (wide-sense) characterization |
1.3_MIDA_Model_Classes |
| 03 08 | Recording | Whiteboard | Non zero mean ARMA Gain theorem Unbiased processes ARMAX Frequency domain Properties of spectrum Spectrum of digital filter output |
1.3_MIDA_Model_Classes 1.4_MIDA_Frequency_Domain_Analysis |
| 03 09 | Recording | Whiteboard | Spectrum antitrasformation Relation between covariance and spectrum Wiener-Kinchin theorem Spectrum of ARMA 4 sources of uniqueness of ARMA |
1.4_MIDA_Frequency_Domain_Analysis |
| 03 10 | Recording | Whiteboard | 4th source of uniqueness of ARMA Canonical representation of ARMA Introduction to linear optimal prediction Mean square error |
1.4_MIDA_Frequency_Domain_Analysis 1.5_MIDA_Prediction |
| 03 15 | Recording | Whiteboard | Optimal linear predictor from noise Long k-step division Optimal linear predictor from output |
1.5_MIDA_Prediction |
| 03 16 | Recording | Whiteboard | Reconstructing WN from output values Predictors from finite sequence of values Optimal prediction of non-sero mean ARMA |
1.5_MIDA_Prediction |
| 03 22 | Recording | Whiteboard | ARMAX predictors Model identification introduction Black box and grey box model identification PEM identification |
1.5_MIDA_Prediction 1.6_MIDA_Identification |
| 03 23 | Recording | Whiteboard | PEM identification cost function PEM cost function computation Least square identification |
1.6_MIDA_Identification |
Exercise sessions
| Date | Recording | Notes | Title |
|---|---|---|---|
| 02 24 | Recording | Notes | Mean and covariance of MA and AR |
| 03 03 | Recording | Notes | AR/ARMA weak characterization Bias proces Final value theorem |
| 03 07 | Recording | Notes | Past exam exercises on MA and ARMA |
| 03 14 | Recording | Notes | Computing the spectrum (4 methods) |
| 03 17 | Recording | Notes | Computing and drawing spectrum |
| 03 21 | Recording | Notes | Prediction |
| 03 24 | Recording | Notes | Predictor of process non-zero mean Predictor of ARMAX |
| 03 28 | Recording | Notes | Introduction to identification Exercises on identification |