Masterstudium Business Informatics - Fakultät für Informatik, TU Wien

• Input/Output modeling (Black Box modeling)
• Discrete transfer functions and Markov models versus general nonlinear discrete
models and classications of the models with respect to nonlinear properties and
data availability
• System dynamics, Forrester's modeling approach (industrial dynamics, system dynamics)
sketched as qualitative and quantitative white-box modeling approach
• Soft computing techniques
• Cellular automata modeling: Modeling with spatial aspects and multidomain modeling
by coupling cellular automata models with system dynamics models
• Discrete event modeling: Statistical features and asynchronous time base modeling
(time event oriented modeling) into modeling of discrete dynamical systems
• Validation and identication: Procedures for model validation and algorithms for
identication
• Advanced modelled and simulation tasks comparing dierent modeling techniques
• Complimentary modules as crash courses for modeling and simulation with MAT-
LAB, Simulink, SimEvents, AnyLogic, and others (e.g., DSOL) and a review of
mathematical algorithms (basics ODE solver, solutions of dierence equations)
Expected Prerequisites: Medium knowledge in mathematical analysis and programming,
basic knowledge in statistics and numerical algorithms.
Teaching and Learning Methods and Adequate Assessment of Performance: The module
is organized along lectures on modeling and simulation concepts, exercises on modeling
and simulation examples with MATLAB, Simulink, Java, and AnyLogic using the webbased
MMT E-Learning system to experiment with partly pre-implemented models to
become familiar with model features and concepts by using personal notebooks, case
studies, students' projects in groups on modeling and simulation.
Courses of Module:
6.0/4.0 VU Modeling and Simulation
FMF/KBS - Knowledge-based Systems
ECTS-Credits: 6.0
Summary: This module oers an introduction into important concepts of knowledgebased
systems like problem solving techniques, formalisms to represent knowledge, and
corresponding deduction concepts. Students acquire systematic knowledge about the
fundamental principles underlying knowledge-based systems, both from a theoretical
and from a practical implementation side. Students continue to train their capabilities
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