Title:  Modelling and Simulation 

Code:  IMS 

Ac.Year:  2010/2011 

Term:  Winter 

Curriculums:  

Language of Instruction:  Czech 

Public info:  http://www.fit.vutbr.cz/study/courses/IMS/public/ 

Private info:  http://www.fit.vutbr.cz/study/courses/IMS/private/ 

Credits:  5 

Completion:  credit+exam (written) 

Type of instruction:  Hour/sem  Lectures  Sem. Exercises  Lab. exercises  Comp. exercises  Other 

Hours:  39  2  0  2  9 

 Examination  Tests  Exercises  Laboratories  Other 

Points:  70  10  0  0  20 



Guarantor:  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 

Prerequisites:  

Substitute for:  

 Learning objectives: 

  The goal is to introduce students to basic simulation methods and tools
for modelling and simulation of continuous, discrete and combined
systems.  Description: 

  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: 


 discrete simulation: using Petri nets, using SIMLIB/C++
 continuous simulation: differential equations, block diagrams, examples of models in SIMLIB/C++
 Syllabus of computer exercises: 

  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 0130986097
 Law A., Kelton D.: Simulation Modelling and Analysis, McGrawHill, 1991, ISBN 0071008039
 Rábová Z. a kol: Modelování a simulace, VUT Brno, 1992, ISBN 8021404809
 Ross, S.: Simulation, Academic Press, 2002, ISBN 0125980531
 Study literature: 

  Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0130986097
 Law A., Kelton D.: Simulation Modelling and Analysis, McGrawHill, 1991, ISBN 0071008039
 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
 
