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ELE620_1

System Identification

This is the study programme for 2019/2020. It is subject to change.


An introduction to stochastic processes and their properties: correlation, white noise and power spectrum. Parametric system identification: ARX and ARMAX models, adaptive filtering, LS and RLS methods. State space models and Kalman filter. Mass and energy balance based models, discretization of continuous systems.

Learning outcome

  • Describe discrete and continuous dynamic systems by transfer-functions and state-space-models.
  • Transform a dynamic system from one form to another.
  • Describe stochastic a system.
  • Do state and parameter estimation by Kalman filtering and RLS.

Contents

An introduction to stochastic processes and their properties: correlation, white noise and power spectrum. Parametric system identification: ARX and ARMAX models, adaptive filtering, LS and RLS methods. State space models and Kalman filter. Mass and energy balance based models, discretization of continuous systems.

Required prerequisite knowledge

None.

Exam

Weight Duration Marks Aid
Written exam1/14 hoursA - FNo printed or written materials are allowed. Approved basic calculator allowed.

Coursework requirements

Exercises
There will be 8 exercises in discipline, those submitted for approval within given deadlines. At least 6 of the exercises should be done and approved by subject teacher within the specified deadlines.

Course teacher(s)

Course coordinator
Karl Skretting
Head of Department
Tom Ryen

Method of work

4 hours of lectures and 1-2 hours exercises per week.

Overlapping courses

Course Reduction (SP)
System identification (MIK130_1) 10
System identification (MIK130_2) 10

Open to

Master studies at the Faculty of Science and Technology.

Course assessment

Form and/or discussion.

Literature

Documents and lecture notes will be available on Canvas.


This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 13.11.2019

History