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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
  • 052350 - SYSTEM IDENTIFICATION AND PREDICTION
Docente Savaresi Sergio Matteo
Cfu 5.00 Tipo insegnamento Modulo Di Corso Strutturato

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*AZZZZ052351 - MODEL IDENTIFICATION AND DATA ANALYSIS
054420 - SYSTEM IDENTIFICATION AND PREDICTION
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 general 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 of dynamical systems, with a focus on black-box system identification. Both linear and non-linear systems are considered. Prediction and forecasting are emphasized in time-series analysis and modelling as important applications of system identification. Each technique is treated both theoretically and practically through application examples.

 


Risultati di apprendimento attesi

Through theoretical lectures and numerical-exercises sessions, the students are expected to:

  • Understand the fundamental problems of statistical learning for dynamical systems, that can be encountered while extracting information from a finite set of observations taken from an unknown system.
  • Learn the main tools of dynamical system identification.
  • Be able to formulate identification problems corresponding to different applications.
  • Understand a range of system identification and prediction algorithms along with their strengths and weaknesses.
  • Be able to apply 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.

  • 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 (algorithm, analysis, validation)
  • 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 (N-ARX and N-ARMAX models) of black-box system-identification

Prerequisiti
  • Familiarity with basic concepts of dynamical systems theory and computer science (algorithms and complexity).
  • 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 problems of moderate complexity.

Bibliografia
Risorsa bibliografica obbligatoriaSergio M. Savaresi, Lecture notes
Risorsa bibliografica facoltativaSergio Bittanti, Model Identification and Data Analysis, Editore: Wiley, Anno edizione: 2019
Risorsa bibliografica facoltativaP. van Overschee, B.L. de Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications, Editore: Kluwer Academic Publishers ftp://ftp.esat.kuleuven.be/pub/SISTA/ida/reports/96-26a.pdf
Risorsa bibliografica facoltativaL. Ljung, System Identification: theory for the user , Editore: Prentice-Hall
Risorsa bibliografica facoltativaT.S. Soderstrom, P.G. Stoica, System Identification, Editore: Prentice-Hall

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
30:00
45:00
Esercitazione
20:00
30:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 50:00 75:00

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