Methods and Algorithms for System Simulation and Optimization (FSI-9MAS)

Academic year 2022/2023
Supervisor: prof. RNDr. Ing. Jiří Šťastný, CSc.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: Czech or English
Aims of the course unit:
The aim of the course is to make students familiar with the methods and selected software supporting the computer simulation.
Learning outcomes and competences:
Students will be able to use software methods and applications for simulation.
Prerequisites:
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.
Course contents:
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.
Teaching methods and criteria:
The course is taught through lectures explaining the basic principles and theory of the discipline.
Assesment methods and criteria linked to learning outcomes:
Exam has a written and an oral part and tests students’ knowledge of the subject-matter covered in the course.
Controlled participation in lessons:
Attendance at seminars is checked by means of projects.
Type of course unit:
    Lecture  10 × 2 hrs. optionally                  
Course curriculum:
    Lecture 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.
Literature - fundamental:
1. Fishwick, P.: Simulation Model Design and Execution, Building Digital Worlds, Prentice-Hall, 1995
2. Zeigler, B., Praehofer, H., Kim, T.: Theory of Modelling and Simulation, 2nd edition, Academic Press, 2000
3. Norgaard, M.: Neural Networks for Modelling and Control of Dynamic Systems, Springer, 2000
4. Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addisson-Wesley Professional,1989
Literature - recommended:
1. Ross, S.: Simulation, 3rd edition, Academic Press, 2002
2. Mandic, Danilo P.: Recurrent neural networks for prediction, learning algorithms, architectures and stability, Wiley, Chichester 2001
3. O´Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary automatic programming in an arbitrary language. Kluwer Academic publishers, 2003
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
D-KPI-K combined study --- no specialisation -- DrEx 0 Recommended course 3 1 S
D-KPI-P full-time study --- no specialisation -- DrEx 0 Recommended course 3 1 S