System Identification Syllabus

 

Course Description

Inferring models from observations and studying their properties is really what science is about. System identification deals with the problem of building mathematical models of dynamical systems based on the observed data from the system. Topics include:

  • Available techniques of identification.
  • The properties of the identified model.
  • Numerical scheme for computing the estimate.
  • How to make intelligent choices of experiment design, model set, and identification criterion, guided by prior information as by observed data.

Prerequisites

  • Basic understanding of linear algebra.
  • Basic understanding of advanced mathematics.
  • Course assumes a working knowledge of MATLAB®

 

Lectures(2017)

  1. Perspective on System Identification.
  2. An Introduction to Statistics
  3. Non-Parametric System Identification. (Ferquency Domain Approach and Correlation Approach)

Lectures(2015)

  1. Perspective on System Identification.
  2. An Introduction to Statistics.
  3. Classical System Identification. (Deterministic System Identification)
  4. Non-Parametric System Identification. (Correlation Approach and Frequency Domain Method)
  5. Linear Parametric Models for LTI Systems.
  6. Suitable Input Signals for System Identification.   Mid term
  7. Parameter Estimation Methods.
  8. Computing the Estimate.   Projects.
  9. Recursive Etimation
  10. Methods. (Real-time Identification).
  11. Nonlinear System Identification.
  12. State Space System Identification.
  13. Data for Projects.

 

Lectures(2014)

 

  1. Perspective on System Identification.
  2. An Introduction to Statistics.
  3. Classical System Identification. (Deterministic System Identification)
  4. Non-Parametric System Identification. (Correlation Approach and Frequency Domain Method)
  5. Parameter Estimation Methods.
  6. Least Square and Numercial Parameter Estimation.
  7. Recursive Etimation Methods. (Real-time Identification).
  8. State Space System Identification.
  9. Nonlinear System Identification.
  10. Under Construction.


Lectures(2013)

  1. Perspective on System Identification.
  2. An Introduction to Statistics.
  3. Linear Regression Properties.
  4. Classical System Identification Methods.
  5. Models of Linear Time Invariant Systems.
  6. Kalman Filter.
  7. Parameter Estimation Methods.
  8. System Idenfication by Using Correlation Function and Suitable Input Signals.
  9. Least Square Estimation.
  10. Recursive Estimation Method.
  11. Models for Nonlinear Systems.
  12. State Space System Identification.
  13. Interactive Multiple Models.

 

 

Homework

 

Homework papers are supposed to be submitted before the deadline.

Late homework will be accepted only in extraordinary circumstances, and may in any case be penalized.

You may work on homework problems in groups of 2-3 people. However you must always  write up the solutions on your own. Similarly, you may use references or other sources to help solve homework problems, but you  must  write up the solution on your own and cite your sources. Copying solutions or codes, in whole or in part, from other students or any other source without acknowledgment will be considered a case of academic dishonesty.

 

 

References:

 

System Identification Theory for the user by Lennar Ljung., second edition, 1999, published by  Prentice  hall.

System Modeling and Identification by Rolf Johansson, second edition, 2010.

Nonlinear System Identification by Oliver Nelles, 2000, Springer

شناسایی سیستم ، مهدی کراری