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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 051587 - MODEL IDENTIFICATION AND DATA ANALYSIS
Docente Bittanti Sergio , 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 (263) MUSIC AND ACOUSTIC ENGINEERING*AZZZZ090037 - MODEL IDENTIFICATION AND DATA ANALYSIS - 1ST MODULE
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA*AZZZZ051587 - MODEL IDENTIFICATION AND DATA ANALYSIS
090038 - MODEL IDENTIFICATION AND DATA ANALYSIS - 2ND MODULE
090037 - MODEL IDENTIFICATION AND DATA ANALYSIS - 1ST MODULE
Ing Ind - Inf (Mag.)(ord. 270) - MI (486) ENGINEERING PHYSICS - INGEGNERIA FISICA*AZZZZ051587 - MODEL IDENTIFICATION AND DATA ANALYSIS
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA*AZZZZ096297 - 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. The course covers advanced topics of data-driven black-box system identification and virtual  sensing.


Risultati di apprendimento attesi

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

  • Understand the fundamental problems that can be encountered while extracting information from a finite set of observations taken from an unknown system.
  • Learn the main tools of system identification and virtual sensing.
  • Be able to formulate 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:

  • Basic concepts of stochastic processes
  • ARMA and ARMAX classes of parametric models for time series and for Input/Output systems
  • Parameter identification of ARMA and ARMAX models
  • Analysis of identification methods
  • Model validation and pre-processing

Part 2

  • Non-parametric system identification: the subspace-based state-space approach
  • Kalman Filter: prediction, virtual-sensing and gray-box system identification
  • Analysis and design of closed-loop systems using the minimum-variance approach
  • Non-linear system-identification: parametric nonlinear fitting; N-ARMAX models; optimal design of basis functions using Principal-Component-Analysis
  • Frequency-domany parametric estimation of models from data
  • Recursive system identification: extension of system-identification to time-varying systems

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 in general;
  • select and describe the most appropriate tools for a given application;
  • use analytical methods to solve system-identification problems of moderate complexity.

Bibliografia
Risorsa bibliografica obbligatoriaS. Bittanti, Model Identification and Data Analysis, Editore: John Wiley, Anno edizione: 2018, ISBN: 9781119546368
Risorsa bibliografica obbligatoriaS. Bittanti and S.M. Savaresi, Lecture notes
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. 1.6.1 / 1.6.1
Area Servizi ICT
08/12/2019