Course detail
Methods and Algorithms for System Simulation and Optimization
FSI-9MAS Acad. year: 2024/2025 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
Supervisor
Department
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.
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.
The study programmes with the given course
Programme D-KPI-K: Design and Process Engineering, Doctoral, recommended course
Programme D-KPI-P: 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.