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 Scheda Riassuntiva
 Anno Accademico 2018/2019 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 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
 Sergio M. Savaresi, Lecture notes P. 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 L. Ljung, System Identification: theory for the user , Editore: Prentice-Hall T.S. Soderstrom, P.G. Stoica, System Identification, Editore: Prentice-Hall

 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 24/09/2023