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

Sommersemester 2021

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Erster Termin der LV
02.03.2021 10:00 - 12:00 Online Off Campus
... keine weiteren Termine bekannt

Überblick

Bedingt durch die COVID-19-Pandemie können kurzfristige Änderungen bei Lehrveranstaltungen und Prüfungen (z.B. Absage von Präsenz-Lehreveranstaltungen und Umstellung auf Online-Prüfungen) erforderlich sein.

Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter: https://www.aau.at/corona.
Lehrende/r
LV-Titel englisch
Optimisation and Neural Network based Simulation Lab for Transportation and Logistics
LV-Art
Kurs (prüfungsimmanente LV )
LV-Modell
Onlinelehrveranstaltung
Semesterstunde/n
2.0
ECTS-Anrechnungspunkte
3.0
Anmeldungen
6 (30 max.)
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
02.03.2021
eLearning
zum Moodle-Kurs
Seniorstudium Liberale
Ja

Zeit und Ort

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LV-Beschreibung

Intendierte Lernergebnisse

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.

Lehrmethodik inkl. Einsatz von 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.

Inhalt/e

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


Prüfungsinformationen

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.

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • 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)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • 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)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • 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)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • 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)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

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)