Modelling and Simulation

Public info:http://www.fit.vutbr.cz/study/courses/IMS/public/
Private info:http://www.fit.vutbr.cz/study/courses/IMS/private/
Completion:accreditation+exam (written)
Type of
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Guarantee:Peringer Petr, Dr. Ing., DITS
Lecturer:Peringer Petr, Dr. Ing., DITS
Instructor:Hrubý Martin, Ing., Ph.D., DITS
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Algorithms (IAL), DIFS
Discrete Mathematics (IDA), DMAT
Introduction to Programming Systems (IZP), DIFS
Mathematical Analysis (IMA), DMAT
Numerical Methods and Probability (INM), DMAT
Signals and Systems (ISS), DCGM
Substitute for:
Modelling and Simulation (MSI), DITS
Learning objectives:
  The goal is to introduce students to basic simulation methods and tools for modelling and simulation of continuous, discrete and combined systems.
  Introduction to modelling and simulation concepts. System analysis and classification. Abstract and simulation models. Continuous, discrete, and combined models. Heterogeneous models. Using Petri nets and finite automata in simulation. Pseudorandom number generation and testing. Queuing systems. Monte Carlo method. Continuous simulation, numerical methods, Modelica language. Simulation experiment control. Visualization and analysis of simulation results.
Knowledge and skills required for the course:
  Basic knowledge of numerical mathematics, probability and statistics, and basics of programming.
Learning outcomes and competences:
  Knowledge of simulation principles. The ability to create simulation models of various types. Basic knowledge of simulation system principles.
Syllabus of lectures:
  • Introduction to modelling and simulation. System analysis, clasification of systems. System theory basics, its relation to simulation.
  • Model classification: conceptual, abstract, and simulation models. Heterogeneous models. Methodology of model building.
  • Simulation systems and languages, means for model and experiment description. Principles of simulation system design.
  • Parallel process modelling. Using Petri nets and finite automata in simulation.
  • Models o queuing systems. Discrete simulation models. Model time, simulation experiment control.
  • Continuous systems modelling. Overview of numerical methods used for continuous simulation. System Dymola/Modelica.
  • Combined simulation. The role of simulation in digital systems design.
  • Special model classes, models of heterogeneous systems.
  • Cellular automata and simulation.
  • Checking model validity, verification of models. Analysis of simulation results.
  • Simulation results visualization. Model optimization.
  • Generating, transformation, and testing of pseudorandom numbers. Stochastic models, Monte Carlo method.
  • Overview of commonly used simulation systems.
Syllabus of numerical exercises:
  1. discrete simulation: using Petri nets, using SIMLIB/C++
  2. continuous simulation: differential equations, block diagrams, examples of models in SIMLIB/C++
Syllabus of computer exercises:
  1. Introduction to Dymola simulation system, continuous simulation.
Syllabus - others, projects and individual work of students:
 Individual selection of a suitable problem, its analysis, simulation model creation, experimenting with the model, and analysis of results.
Fundamental literature:
  • Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0-13-098609-7
  • Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991, ISBN 0-07-100803-9
  • Rábová Z. a kol: Modelování a simulace, VUT Brno, 1992, ISBN 80-214-0480-9
  • Ross, S.: Simulation, Academic Press, 2002, ISBN 0-12-598053-1
Study literature:
  • Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0-13-098609-7
  • Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991, ISBN 0-07-100803-9
  • Texts available on WWW.
Controlled instruction:
  Within this course, attadance on the lectures is not monitored.
The knowledge of students is examined by the projects and
by the final exam. The minimal number of points which
can be obtained from the final exam is 30. Otherwise,
no points will be assigned to a student.
Progress assessment:
  project, midterm exam
Exam prerequisites:
  At least half of the points you can get during the semester