700.372 (21S) Optimisation and Neural Network based Simulation Lab for Transportation and Logistics

Sommersemester 2021

Registration deadline has expired.

First course session
02.03.2021 10:00 - 12:00 Online Off Campus
... no further dates known

Overview

Due to the COVID-19 pandemic, it may be necessary to make changes to courses and examinations at short notice (e.g. cancellation of attendance-based courses and switching to online examinations).

For further information regarding teaching on campus, please visit: https://www.aau.at/en/corona.
Lecturer
Course title german Optimisation and Neural Network based Simulation Lab for Transportation and Logistics
Type Course (continuous assessment course )
Course model Online course
Hours per Week 2.0
ECTS credits 3.0
Registrations 6 (30 max.)
Organisational unit
Language of instruction English
Course begins on 02.03.2021
eLearning Go to Moodle course
Seniorstudium Liberale Yes

Time and place

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.
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Course Information

Intended learning outcomes

This lecture familiarizes students with the fundamentals of optimization and neural networks. Selected applications are considered in various fields of engineering including transportation.

The general expectation regarding the knowledge to be provided/acquired is as follows:

  • Mastering of the basics of optimization and selected applications
  • Mastering of the basics of neural networks and selected applications
  • Mastering of some MATLAB Toolboxes (e.g. Linear programming and Quadratic programming toolboxes) and their application in solving linear and nonlinear optimization problems.
  • Mastering of Recurrent Neural Networks and their application in solving linear and nonlinear optimization problems.
  • Mastering of the use of Neural networks to solve algebraic equations
  • Mastering of the use of Neural networks for traffic flow counting
  • Mastering of the use of Neural networks to implement logic gates
  • Mastering of the development of simulation algorithms (based on Recurrent Neural Networks) for the solving of shortest path problems in graph networks.
  • Mastering of the development of simulation algorithms (based on Recurrent Neural Networks) for the solving of traveling salesman problems in graph networks.

Teaching methodology including the use of eLearning tools

The slides are available for the entire lecture. These slides are uploaded into the MOODLE system. The entire content of each slide is systematically explained by the lecturer.

Additional examples that are not included in the slides are suggested by the lecturer to allow a good understanding of the information provided.

The slides contain exercises with solutions to allow a good understanding of the contents of each chapter. These solutions are systematically explained (during the lecture) by the lecturer.

The Lecturer provides full explanation of how to write numerical codes to solve the exercises proposed in each chapter of the Lecture.

Course content

The lecture is organized around the following topics:

1. Fundamentals of optimization

2. Fundamentals of Neural Networks and Recurrent Neural Networks

3. Models of artificial neurons

4. Learning mechanism

5. Single-layer perceptron

6. Multi-layer perceptron

7. Neural Networks based linear optimization

8. Neural Networks based quadratic optimization

9. Neural Networks based solving of algebraic equations

10. Neural Networks based traffic flow counting

11. Neural Networks based implementation of logic gates (AND, NAND, OR, NOR, XOR. XNOR)

12. Neural Networks based high order nonlinear optimization

13. Neural Networks based shortest path detection

14. Neural Networks based travel salesman problem detection

15. Neural Networks based - Binary Classification  

16. Radial basis function networks

17. Principal component analysis

18. Self-organizing map


Examination information

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Information and Communications Engineering: Supplements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Technical Complements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Information and Communications Engineering: Supplements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Technical Complements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)

Equivalent courses for counting the examination attempts

Sommersemester 2023
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2022/23
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Sommersemester 2022
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Sommersemester 2020
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2018/19
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2017/18
  • 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2016/17
  • 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2015/16
  • 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2014/15
  • 700.372 KU Simulation Lab for Transportation and Logistics (1.0h / 1.5ECTS)
Wintersemester 2013/14
  • 700.372 KU Simulation Lab for Transportation and Logistics (1.0h / 1.5ECTS)
Wintersemester 2012/13
  • 700.372 KU Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)