BCT-31806 Parameter Estimation and Model Structure Identification


Credits 6.00

Teaching methodContact hours
Independent study
Course coordinator(s)Prof. dr. ir. KJ Keesman
dr. ir. LG van Willigenburg
Lecturer(s)Prof. dr. ir. KJ Keesman
dr. ir. LG van Willigenburg
Examiner(s)Prof. dr. ir. KJ Keesman
dr. ir. LG van Willigenburg

Language of instruction:


Assumed knowledge on:

BCT-20306 Modelling Dynamic Systems.

Continuation courses:

BCT-31306 Systems and Control Theory; BCT-21306 Control Engineering; BCT-30806 Physical Modelling.


Mathematical models are crucial in many scientific fields. Modelling that starts from first principles can then be a good start. For operational use in real practice, however, the model should be in good accordance with experimental data.
The aim of this course is to introduce methods for the determination or identification of static/dynamic models starting from prior system's knowledge, given data and the model objective, and to estimate the unknown parameters. In practice, the main model objectives are prediction, experiment design and management/control, which will receive ample attention.
The main application fields are the agro/biotechnology, environmental sciences and food science. In particular, attention will be paid to parameter estimation and network identification within biobased sciences.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- identify a simple dynamic process model from given input/output signals;
- model systems on the basis of prior system's knowledge and experimental input/output data, and to estimate the unknown model parameters;
- simulate and analyse 'simple' models from systems biology and to estimate the unknown parameters;
- apply the 'System Identification Toolbox' of the Matlab software package.


- lectures to provide some additional background material;
- computer practicals to apply the theory in a Matlab environment.


Observations during practicals. The practical report is judged with a mark.
Exam consist of the average of the take home exams (60%) and the practical report (40%) with scores of at least 5.5.


Course information
K.J. Keesman (2011). System Identification: an Introduction. London. Springer. 323p. ISBN 978-0-85729-522-4. Series: Advanced Textbooks in Control and Signal Processing.
Additional material will be available at the start of the course.

Restricted Optional for: MBTBiotechnologyMScD: Process Technology4WD
MBEBiosystems EngineeringMSc4WD