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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2019/2020
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 052351 - MODEL IDENTIFICATION AND DATA ANALYSIS
Docente Formentin Simone , Savaresi Sergio Matteo
Cfu 10.00 Tipo insegnamento Corso Integrato

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - MI (473) AUTOMATION AND CONTROL ENGINEERING - INGEGNERIA DELL'AUTOMAZIONE*AZZZZ054420 - SYSTEM IDENTIFICATION AND PREDICTION
052351 - MODEL IDENTIFICATION AND DATA ANALYSIS
Ing Ind - Inf (Mag.)(ord. 270) - MI (476) ELECTRONICS ENGINEERING - INGEGNERIA ELETTRONICA*AZZZZ052351 - MODEL IDENTIFICATION AND DATA ANALYSIS
Ing Ind - Inf (Mag.)(ord. 270) - MI (486) ENGINEERING PHYSICS - INGEGNERIA FISICA*AZZZZ052351 - MODEL IDENTIFICATION AND DATA ANALYSIS

Obiettivi dell'insegnamento

The goal of the course is to enable students to extract (“learn”) from measured data useful information for automation, systems and control applications. The course covers the fundamentals of data-driven learning, system identification and virtual sensing.  Each technique is treated both theoretically and practically through computer exercises with real-world data.


Risultati di apprendimento attesi

Through theoretical lectures and computer sessions, the students are expected to:

  • understand the fundamental problems, shared by applied statistics, machine learning and system identification, that can be encountered while extracting information from a finite set of observations taken from an unknown system;
  • learn the main tools of statistical learning and system identification;
  • be able to formulate learning/identification problems corresponding to different applications;
  • understand a range of algorithms along with their strengths and weaknesses;
  • be able to apply suitable algorithms to solve application problems;
  • be able to read current research papers and understand the issues raised by current research in the field.

Argomenti trattati

The program of the course is as follows.

Part 1 (Statistical learning for Automation Systems):

  • Introduction to statistical learning: main definitions and comparison with similar disciplines
  • The mathematical foundations of learning
  • Learning of static models
  • Data-preprocessing
  • Clustering
  • Towards learning of dynamical models
  • Virtual sensing: the Kalman filter

Part 2 (System Identification and Prediction):

  • Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (Box-Jenkins models, and sub-classes like ARX and ARMAX)
  • Linear optimal prediction
  • Black-box identification from measured data-sets of Input/Output models
  • Black-box identification from measured data-sets of state-space models
  • Gray-box system-identification using Extended Kalman Filter
  • Frequency-domany parametric estimation of models from data
  • Extension to non-linear dynamical systems of black-box system-identification

Prerequisiti
  • Familiarity with basic concepts of computer science (algorithms and complexity) and dynamical systems theory.
  • Mathematical maturity in linear algebra and probability theory.

Modalità di valutazione

The exam will be a written test in one of the official available dates. The exam will consist of both theoretical questions and simple  (i.e., no computer-aided) exercises. The final marks will be awarded based on correctness of the answers, appropriateness of technical terminology and clarity of exposition. Specifically, the students will be asked to:

  • show their understanding of the main problems (and the corresponding countermeasures) in the field of data-driven modeling and learning in general;
  • select and describe the most appropriate tools for a given application;
  • use analytical methods to solve learning problems of moderate complexity.

Bibliografia
Risorsa bibliografica obbligatoriaS. Formentin and S.M. Savaresi, Lecture notes
Risorsa bibliografica facoltativaSergio Bittanti, Model Identification and Data Analysis, Editore: Wiley, Anno edizione: 2019
Risorsa bibliografica facoltativaY.S. Abu-Mostafa, M. Magdon-Ismail, H.T. Lin, Learning from data, Editore: AML Book, Anno edizione: 2012
Risorsa bibliografica facoltativaG. James. D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, Editore: Springer, Anno edizione: 2013 http://www-bcf.usc.edu/gareth/ISL/
Note:

Freely available online.

Risorsa bibliografica facoltativaM.S. Grewal, A.P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, Editore: Wiley Online Library, Anno edizione: 2008 http://read.pudn.com/downloads148/ebook/638857/Kalman%20Filtering%20-%20Theory%20and%20Practice%20Using%20MATLAB,%203rd%20Ed.pdf
Note:

Freely available online.

Risorsa bibliografica facoltativaP. van Overschee, B.L. de Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications, Editore: Kluwer Academic Publishers, Anno edizione: 1996 ftp://ftp.esat.kuleuven.be/pub/SISTA/ida/reports/96-26a.pdf
Note:

Freely available online.

Risorsa bibliografica facoltativaL. Ljung, System Identification: theory for the user (II ed.), Editore: Prentice-Hall, Anno edizione: 1999
Risorsa bibliografica facoltativaT.S. Soderstrom, P.G. Stoica, System Identification, Editore: Prentice-Hall, Anno edizione: 1989

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
60:00
90:00
Esercitazione
40:00
60:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 100:00 150:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.5 / 1.6.5
Area Servizi ICT
30/11/2020