Slovensko

Modelling Methods

Higher education teachers: Atanasijević-Kunc Maja



Subject description

Prerequisits:

Inscription to 2nd cycle masters study programme in Electrical Engineering

Content (Syllabus outline):

  • introduction (the reasons for the construction of models, basic definitions);
  • introduction of similarities or analogies and their importance in the context of systems engineering;
  • illustration of the importance of modelling with examples from the field of electrical engineering, mechanics, hydraulics, and pneumatics, thermodynamics, economics , medicine, pharmacy and biology, system control and fault detection;
  • analysis and model simplification (structural simplification and linearization);
  • specific types of models (compartment models, bond graphs, hybrid models, some of AI approaches);
  • intuitively clear approaches: response adaptation methods, model based fault detection;
  • conventional approaches of models‘ optimization and usage of evolutionary computation;
  • usage of already known software tools (Matlab, Simulink , Control System Toolbox) and the presentation of some additional toolboxes in Matlab and in some other programs, suitable for the so called system dynamics and visualization.

Objectives and competences:

  • to present advanced knowledge in the field of process modelling;
  • to accent the wide and multidisciplinary nature of the area and thus its broad meaning;
  • to present typical and also some specific forms of models and their scope;
  • to present some of the software tools and their usefulness in support of the issue being discussed.

Intended learning outcomes:

Gained knowledge will enable:

  • a systematic approach to problem solving by modelling (theoretical , experimental and combined);
  • properties’ determination of dynamic mathematical models;
  • analytical and simulation experimentation using linear and non-linear models and the implementation of mutual comparison;
  • optimization of mathematical models using conventional methods and evolutionary algorithms;
  • qualitative and quantitative evaluation of developed models;
  • the use of theoretical approaches in modelling real systems.

Learning and teaching methods:

  • Lectures and
  • laboratory exercises,
  • project work





Study materials

Readings:

  1. Matko D, Karba R, Zupančič B. Simulation and modelling of continuous systems, A case study approach. New York: Prentice Hall, 1992.
  2. Cellier F. Continuous system modeling. New York: Springer-Verlag; 1991.
  3. Boyd DW. Systems analysis and modeling, A macro-to-micro approach with multidisciplinary applications. San Diego: Academic Press; 2001.
  4. Hoppensteadt FC, Peskin CS. Modeling and simulation in medicine and the life sciences. Second Edition. Springer; 2004.
  5. Monsef Y. Modelling and simulation of complex systems, concepts, methods and tools. Erlangen: Society for Computer Simulation Int.; 1997.
  6. Walter GG, Contreras M. Compartmental modeling with networks, Boston: Birkhäuser; 1999.







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