Linear Stochastic Systems

- A geometric approach to modeling, estimation and identification

Spring 2007

• Information about the course
• Lecturer
• Plan for the classes
• Literature: latest version (jan 28, 2007)
  • List of changes, here are corrections and modifications to the material.
• Lecture notes: These notes are the ones I use for the lectures and if they are of some use I make them available here after the classes.
  • Class 1
  • Class 2
  • Class 3
  • Class 4
  • Class 5
  • Class 6
  • Class 7
  • Class 8
  • Class 9
  • Class 10
• Homework 1. You can hand in the homeworks on march 20, or earlier. While correcting the homeworks, I found an error in the exercise on Wiener filters. Therefore, I wrote this note wiener to clarify this issue.
• Homework 2. The homeworks are now ready. This time they are more applied and you will need to use Matlab. Still, the exercises are only simple applications of the theory, and the methods used are just for illustrating the theory in the simplest way, and I do not recommend that you use these approaches to design any airplane controls. I recommend the seminar by Alessandro Chiuso on the 11:th of May for a view of good use of the theory.
• Oral exam. Finish the homeworks and then contact me to book a date for the exam. There is a lot of material in the book, so to help you a bit I wrote down the things I think are most important: study guide.
• Exercises. Some of these exercises will form the home assignments of the course.
  Data files:
  • y_data.mat for exercise 6.8. Contains the variables:
    • y - 5000 outputs of a simulated eigth order linear model
    • xgen, ygen - (x,y) data of the logarithm of the spectral density of the generating model
    Read the variables into Matlab by writing "load y_data". (first download the data file to the current Matlab directory).
  • modelmag.m Matlab file that can be used to plot the spectral density corresponding to a transfer function.
  • model83.mat for exercise 8.3.
  • model4.mat for exercise 4 in homework 2.