Experimental Identification - AP445012
Title: Experimental Identification
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2019 to 2020
Semester: both
Points: 0
E-Credits: 0
Examination process:
Hours per week, examination: 3/0, other [HT]
Capacity: winter:unlimited / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Teaching methods: full-time
Note: can be fulfilled in the future
you can enroll for the course in winter and in summer semester
Guarantor: Mareš Jan prof. Ing. Ph.D.
Interchangeability : N445021, P445012
Examination dates   Schedule   
Annotation -
The main goal of the course is to familiarize the students with the common generally valid principles of system identification both analogue and discrete
Last update: Pátková Vlasta (19.11.2018)
Aim of the course -

Students will be able to:

• work with common identification methods

• work with their implementation

• evaluate their efficiency

• students will take part in one large complex work (beginning with data measuring up to model formulation)

• students will use MATLAB interface for model identification

Last update: Pátková Vlasta (19.11.2018)
Literature -

R:Tangirala A.K., Principles of System Identification:Theory and Practice, CRC Press, Boca Raton, 2015, ISBN 978-1-4398-9602-0

A:Ljung L.,Systém Identification. Theory for the User,Prentice Hall PTR,N.J.,1999,0136566952

Last update: Mareš Jan (22.04.2020)
Learning resources -

internal materials

Last update: Pátková Vlasta (19.11.2018)
Teaching methods -

lectures, projects and consultations

Last update: Pátková Vlasta (19.11.2018)
Syllabus -

1. Experimental identification - basic scheme, wide and narrow definition of the branch

2. Structure model selection and process dynamics

3. Integral transforms in process identification I

4. Integral transforms in process identification II

5. Least square method, on-line least square method, Extended least square method

6. Strejc- Broid method

7. Advanced statistical methods in identification I

8. Advanced statistical methods in identification II

9. Stochastic models I

10. Stochastic models II

11. Neural nets in identification I

12. Neural nets in identification II

13. Numerical methods in identification

14. Projects

Last update: Mareš Jan (22.04.2020)
Entry requirements -


Last update: Pátková Vlasta (19.11.2018)
Registration requirements -


Last update: Pátková Vlasta (19.11.2018)
Course completion requirements -
  • making all projects
  • final discusion on them with the demonstration of knowledge
Last update: Pátková Vlasta (19.11.2018)