Course details
Modelling and Simulation
IMS Acad. year 2006/2007 Winter semester 5 credits
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. Simulation experiment control. Visualization and analysis of simulation results.
Guarantor
Language of instruction
Completion
Time span
- 39 hrs lectures
- 6 hrs exercises
- 7 hrs projects
Department
Subject specific learning outcomes and competences
Knowledge of simulation principles. The ability to create simulation models of various types. Basic knowledge of simulation system principles.
Learning objectives
The goal is to introduce students to basic simulation methods and tools for modelling and simulation of continuous, discrete and combined systems.
Recommended prerequisites
- Introduction to Programming Systems (IZP)
- Algorithms (IAL)
- Signals and Systems (ISS)
- Algorithms (IAL)
Prerequisite knowledge and skills
Basic knowledge of numerical mathematics, probability and statistics, and basics of programming.
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 Ross, S.: Simulation, Academic Press, 2002, ISBN 0-12-598053-1
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.
- Combined simulation. The role of simulation in digital systems design.
- Special model classes, models of heterogeneous systems.
- Checking model validity, verification of models. Analysis of simulation results.
- Simulation results visualization. Interactive simulation, virtual reality.
- Design and control of simulation experiments. 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
- discrete simulation: using SIMLIB/C++
- continuous simulation: differential equations, block diagrams, examples of models in SIMLIB/C++
Progress assessment
Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.
Controlled instruction
project, midterm exam