Identification of Technological Processes

Lecturer:

Milan Hofreiter (gar.)

Tutor:

Milan Hofreiter (gar.)

Synopsis:

The subject is aimed to explanation of basic identification methods to obtain mathematical description of deterministic and stochastic systems. Experimental identification methods are explained for linear stochastic and deterministic dynamic systems in greater detail. The attention is also devoted to the numerical solution of identification algorithms. The course is concentrated to the most frequent methods which are applied in practice for identification of technological processes.

Requirements:

Fundamental terms and classification of identification methods, Time-invariant linear systems, Experimental identification of nonparametric models, Signal description using a frequency domain representation, Continuous and discrete models of linear time-invariant systems, Estimating parametric models from nonparametric models, Least-squares method, Numerical solutions for the least-squares estimate, Maximum likelihood method, Recursive estimation methods, Models ARMA, AR, MA, ARX, OE, ARMAX, BJ, One-step and multi-step ahead prediction of output, Closed-loop identification, Identification of technological processes in practice

Syllabus of lectures:

1) Fundamental terms and classification of identification methods

2) Time-invariant linear systems

3) Experimental identification of nonparametric models

4) Signal description using a frequency domain representation

5) Continuous and discrete models of linear time-invariant systems

6) Estimating parametric models from nonparametric models

7) Least-squares method

8) Numerical solutions for the least-squares estimate

9) Maximum likelihood method

10) Recursive estimation methods

11) Models ARMA, AR, MA, ARX, OE, ARMAX, BJ

12) One-step and multi-step ahead prediction of output

13) Closed-loop identification

14) Identification of technological processes in practice

Syllabus of tutorials:

Solution of the project, which is aimed to identification of a selected laboratory apparatus by various identification procedures.

Study Objective:

Study materials:

L. Ljung: System Identification - Theory for the User, Prentice Hall PTR, 1999.

Y. Zhu: Multivariable System Identification for Process Control, Elsevier Science Ltd., 2001.

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans: