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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2023/2024
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 051587 - MODEL IDENTIFICATION AND DATA ANALYSIS
  • 051588 - MODEL IDENTIFICATION AND DATA ANALYSIS - 1ST MODULE
Docente Formentin Simone
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 (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA*AZZZZ051587 - MODEL IDENTIFICATION AND DATA ANALYSIS
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*AZZZZ051587 - MODEL IDENTIFICATION AND DATA ANALYSIS

Obiettivi dell'insegnamento

The goal of this course is to provide a framework for advanced modeling and data analysis techniques for dynamical systems, ultimately enabling students to extract (“learn”) useful information from measured data. The course focuses on the following topics: stochastic processes, optimal prediction, parametric and non-parametric system identification, time series and input-output systems, state-space methods for multivariable system identification and state estimation (Kalman filtering, virtual sensing), nonlinear model identification methods, recursive and adaptive identification methods. The course has both a theoretical and a practical flavor, with the illustration of some significant applications of the presented techniques in various fields.


Risultati di apprendimento attesi

Knowledge and understanding - Through theoretical lectures and numerical exercises sessions, students will achieve a comprehensive knowledge of advanced modeling and data analysis techniques. In particular, they will learn the main tools of system identification, the fundamental algorithms for adaptive filtering, identification and control, the Kalman filter with various extensions, subspace algorithms for state-space identification, and identification methods for nonlinear systems. Students will also be able to understand the fundamental problems that can be encountered while extracting information from a finite set of observations taken from an unknown system.

 

Applying knowledge and understanding - Students will be able to apply the mentioned methods to practical estimation, identification, prediction, and fault detection problems, using real data measurements. They will also have full awareness of their strengths and weaknesses. 

 

Making judgements - Thanks to the project activity, the student will learn to formulate and solve autonomously a practical identification problem, from analysis to code development and validation.

 

Lifelong learning skills - Students will be able to apply the methodologies learned throughout the course to tackle complex modeling and estimation problems in a structured way. They will also have a sufficiently comprehensive background to be able to learn new methodologies. Students will also 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, 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 facoltativaS. Bittanti, Model Identification and Data Analysis, Editore: John Wiley, Anno edizione: 2018
Risorsa bibliografica obbligatoriaS. Formentin and S.M. Savaresi, Lecture notes

Software utilizzato
Nessun software richiesto

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. 1.8.3 / 1.8.3
Area Servizi ICT
02/03/2024