Course detail
Methods and Algorithms for System Simulation and Optimization
FSI-9MASAcad. year: 2023/2024
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
Czech
Mode of study
Not applicable.
Guarantor
Entry knowledge
Fundamentals of mathematics, including differential and integral calculus of functions in one and more variables and solution of system differential equations. Fundamentals of physics, mechanics, electrical engineering and automatic control, knowledge of basic programming techniques.
Rules for evaluation and completion of the course
Exam has a written and an oral part and tests students’ knowledge of the subject-matter covered in the course.
Attendance at seminars is checked by means of projects.
Attendance at seminars is checked by means of projects.
Aims
The aim of the course is to make students familiar with the methods and selected software supporting the computer simulation.
Students will be able to use software methods and applications for simulation.
Students will be able to use software methods and applications for simulation.
Study aids
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
Fishwick, P.: Simulation Model Design and Execution, Building Digital Worlds, Prentice-Hall, 1995
Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addisson-Wesley Professional,1989
Norgaard, M.: Neural Networks for Modelling and Control of Dynamic Systems, Springer, 2000
Zeigler, B., Praehofer, H., Kim, T.: Theory of Modelling and Simulation, 2nd edition, Academic Press, 2000
Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addisson-Wesley Professional,1989
Norgaard, M.: Neural Networks for Modelling and Control of Dynamic Systems, Springer, 2000
Zeigler, B., Praehofer, H., Kim, T.: Theory of Modelling and Simulation, 2nd edition, Academic Press, 2000
Recommended reading
Mandic, Danilo P.: Recurrent neural networks for prediction, learning algorithms, architectures and stability, Wiley, Chichester 2001
O´Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary automatic programming in an arbitrary language. Kluwer Academic publishers, 2003
Ross, S.: Simulation, 3rd edition, Academic Press, 2002
O´Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary automatic programming in an arbitrary language. Kluwer Academic publishers, 2003
Ross, S.: Simulation, 3rd edition, Academic Press, 2002
Classification of course in study plans
Type of course unit
Lecture
20 hod., optionally
Teacher / Lecturer
Syllabus
1. Introduction to computer simulation and optimization methods.
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.
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.