Course detail

Methods and Algorithms for System Simulation and Optimization

FSI-9MAS Acad. year: 2022/2023 Both semester

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

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.

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.

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.

The study programmes with the given course

Programme D-KPI-P: Design and Process Engineering, Doctoral, recommended course

Programme D-KPI-K: Design and Process Engineering, Doctoral, recommended course

Type of course unit

 

Lecture

20 hours, optionally

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.