Course detail

Methods and Algorithms for System Simulation and Optimization

FSI-9MASAcad. year: 2021/2022

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

prof. RNDr. Ing. Jiří Šťastný, CSc.

Department

Institute of Automation and Computer Science (ÚAI)

Learning outcomes of the course unit

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.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

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.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The aim of the course is to make students familiar with the methods and selected software supporting the computer simulation.

Specification of controlled education, way of implementation and compensation for absences

Attendance at seminars is checked by means of projects.

Recommended optional programme components

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

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

Classification of course in study plans

  • Programme D-KPI-P Doctoral 1 year of study, summer semester, recommended course
  • Programme D-KPI-K Doctoral 1 year of study, summer semester, recommended course

Type of course unit

 

Lecture

20 hod., optionally

Teacher / Lecturer

prof. RNDr. Ing. Jiří Šťastný, CSc.

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.