EE 521 - System Identification

Announcement (Semester 1, 2023)

  • EE521 is the substitution of EE531 (which is closed permanently).
  • All course information are officially in CourseVille. Homework assignments, data files, or other online resources are in CourseVille.
  • You can use this page to download course handouts only.

Book

System Identification Book by Jitkomut Songsiri

  • The book is the main reference of this course (written in Thai). Students can purchase the book from the catalog link in the book’s page. It is available in both color-printed version and e-book.

Lecture videos

Videos playlist on YouTube: System identification: parametric approach

Term Project

Each year we aim to assign the problems of fitting models to real-world data as group assignments. Students learn a general description of the problem before the midterm and the project starts after the midterm. Students develop the problem statement, estimation formulation, experimental results and write the report throughout the six weeks until the final week. Each group writes a progress report and present it weekly. Students should apply the estimation techniques learned from class or more advanced/alternative methods from research literature. The full reports from the previous years include:

Project 2018

  1. Project description and weekly details (2018)
  2. Learning Brain Network Differences from EEG Data
    • Contributor: Parinthorn Manomaisaowapak
  3. Photovaltaic System Modeling
    • Contributor: Parinthorn Manomaisaowapak
    • Contributors: Kitinan Boonravee and Jeerapat Jitnuant
  4. Time Series Models of Stock Price
    • Contributor: Parinthorn Manomaisaowapak
    • Contributor: Maytus Piriyajitakonkij

Project 2017

  1. Project description and weekly details (2017)
  2. Modeling of Photovoltaic System
    • Contributors: Janenarong Klomklao
  3. An Identification of Building Temperature System
    • Contributors: Chanthawit Anuntasethakul/ Natthapol Techaphanngam/ Natdanai Sontrapornpol
    • an extension of this project was published in ECTI-CON paper, 2018
  4. An EEG subspace identification
    • Contributors: Satayu Chunnawong
  5. Recursive Estimation of Solar Irradiance using Time-Series Model
    • Contributors: Tony Fang
    • the project was not finished completely

Project 2016

  1. Project description and weekly details (2016)
  2. Estimation of Autoregressive with Exogeneous Inputs Model for fMRI Data
    • Contributors: Nattaporn Plub-in/ Patawee Prakrankamanant/ Nop Polboon/ Ranyaphat Hongpipatsak/ Morokot Cheat
  3. Solar Forecasting using Time Series Models
    • Contributors: Maxime Facquet/ Supachai Suksamosorn/ Veenakorn Suphatsatienkul/ Vichaya Layanun

Project 2015

  1. Project description and weekly details (2015)
  2. A Convex Formulation of Structural Equation Modeling (SEM) in fMRI Study
    • Contributors: Anupon Pruttiakaravanich/ Auangkun Rangsikunpum/ Pusit Suriyavejwongs/ Tawan Luprasong
  3. Parameter estimation of Gumbel distribution for flood peak data
    • Contributors: Piyatida Hoisungwan (co-advisor from Dept. of Water Resource)/ Jitin Khemwong/ Tiwat Boonyawiwat/ Tanakorn Kriengkomol
  4. Rainfall Grid Interpolation from Rain Gauge and Rainfall Predicted from Satellite Data
    • Contributors: Piyatida Hoisungwan (co-advisor from Dept. of Water Resource)/ Petchakrit Pinyopawasutthi/ Pongsorn Keadtipod/ Tanut Aranchayanont
  5. Fitting Vector Auto Regressive Model to Electroencephalography (EEG) Signals
    • Contributors: Nuntanut Raksasri/ Akasit Aupaiboon/ Pawarisson Jaitahan

MATLAB Data Files

Course Information

Lectures:EE404, Mon Wed 8:00-9:30 am
Textbooks:
    1. Songsiri, System Identification, 2021 (in Thai).
    1. Ljung, System Identification: Theory for the User, 2nd edition, Prentice Hall, 1999
    1. Soderstrom and P. Stoica, System Identification, Prentice Hall. 1989 (available for download from the author website)
    1. James and D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013
    1. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, 2009
  • J.P. Norton, An Introduction to Identification, Dover, 1986
  • P.Van Overschee and B.D. Moor, Subspace identification for linear systems: Theory—Implementation—Applications, Kluwer Academic Publishers, 2012
    1. Verhaegen and V. Verdult, Filtering and System Identification: A Least-square Approach, Cambridge University Press, 2007.
Related textbooks:
 
  • P.S.P. Cowpertwait and A.V. Metcalfe, Introductory Time Series with R, Springer, 2009
      1. Shumway and D.S. Stoffer, Time Series Analysis and Its Applicattions: with R Examples, Springer, 2009
  • R.A. Horn and C.R. Johnson, Matrix Analysis, 2nd edition, Cambridge, 2013
Grading:Refer to what has been announced in My CourseVille