Course detail
Methods and Algorithms for System Simulation and Optimization
FSI-9MASAcad. year: 2009/2010
The course deals with the following topics: Classification of elements and systems. Numerical simulation methods. Modelling by means of formal systems, finite automata and Petri nets. Continuous, discrete, mixed and object-oriented simulation systems. Artificial intelligence methods in simulation and optimization. Using neural networks and evolutionary algorithms for classification and prediction.
Language of instruction
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Zeigler, B., Praehofer, H., Kim, T.: Theory of Modelling and Simulation, 2nd edition, Academic Press, 2000
Norgaard, M.: Neural Networks for Modelling and Control of Dynamic Systems, Springer, 2000
Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addisson-Wesley Professional,1989
Recommended literature
Mandic, Danilo P.: Recurrent neural networks for prediction, learning algorithms, architectures and stability, Wiley, Chichester 2001
Ross, S.: Simulation, 3rd edition, Academic Press, 2002
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Classification of elements and systems.
3. Numerical simulation methods.
4. Modelling by means of formal systems.
5. Modelling by means of finite automata and Petri nets.
6. Continuous, discrete, mixed and object-oriented simulation systems.
7. Artificial intelligence methods in modelling and simulation.
8. Artificial intelligence methods in optimization and identification.
9. Using neural networks for classification and prediction.
10. Using evolutionary algorithms for classification and prediction.