Parameter estimation - Paraméterbecslés

Parameter estimation - Paraméterbecslés

(VEMIVIM133P, MI MSc)

1st semester of 2017/2018

Lecture-tutorial-laboratory: Wednesday 11:15-14:00, I/207B

Lecturer: Prof. Katalin Hangos ( Ezt a címet a spamrobotok ellen védjük. Engedélyezze a Javascript használatát, hogy megtekinthesse. )
Tutor: Anna Ibolya Pózna ( Ezt a címet a spamrobotok ellen védjük. Engedélyezze a Javascript használatát, hogy megtekinthesse. )

Course evaluation

The evaluation is based upon a mid-semester closed-book exam and on a parameter estimation project work to be implemented in MATLAB.
The pre-requisite of the course signature is
-- to be present at least 75% of the lectures-tutorials-laboratories, 
-- to submit the project results and documentation to the given deadline, and
-- to achieve at least 50% on the closed-book exam.

Homework points: icon HW-s

Course contents

Week Date Type Material Slides/supplements
1 6 Sept
Registration week
2 13 Sept Lec/Tut Recalling the basic knowledge on scalar and vector valued random variables, their properties and independence. The basics of mathematical statistics: sample, statistics-estimation. The estimation of mean value, covariance and correlation.

icon PB_basic_notions

icon PBtut_basic_notions

3 20 Sept Lab MATLAB basics. Simple functions for estimating mean value, covariance and correlation.

icon PB_Lab_1

icon autocov (131 Bytes)

icon PB_Lab_1_Matlab (3.2 kB)

4 27 Sep Lec/Tut The properties of the estimates. Parameter estimation of static models. Linear regression and its properties.

icon PB_linear_regression

icon PBtut_linear_regression

5 4 Oct Lab Linear regression and its properties.

icon datasets (7.14 kB)

icon PB_Lab_2 (167.18 kB 2017-10-04 16:24:11)

icon leastsquares (631 Bytes)

6 11 Oct Lec/Tut Discrete time stochastic processes, the input-output models of discrete time stochastic linear time-invariant systems. The principle of parameter estimation in dynamic case.

icon PBLinDynMod

icon PBtut_Linear_Dynamic_Models

7 18 Oct Lec/Tut Methods that are based on minimization of the prediction error. Methods based on least squares (LS) minimization. The properties of the LS estimation method.

icon PB_DynLS

icon PB_DynLS_Lab

8 25 Oct Lab Methods based on least squares (LS) minimization.

icon PB_LAB_3 ( 2017-10-25 15:32:28)

icon PB_Lab_3_Matlab ( 2017-10-25 15:32:05)

9 8 Nov Lec/Tut Special parameter estimation methods.           The instrumental variable (IV) method.         Parameter estimation for nonlinear models.
icon PB_DynSpecLS
10 15 Nov Lab Parameter estimation for nonlinear models. The instrumental variable (IV) method.
icon dataset1 icon V
icon dataset2 icon V2
icon gradient
icon PB_LAB_4
12 29 Nov Lec/Tut The practical implementation of parameter estimation. Project work specification. Consultation.

icon PB_PractImpl

icon project

13 6 Dec Lec/Tut
Lab
CLOSED BOOK EXAM
Consultation for the project work

14 13 Dec Lab Submission DEADLINE of the PROJECT WORK

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