|
|
Code: |
214350 |
|
Module title: |
Artificial Neural Networks |
|
Version: |
1.0 (03/2016) |
| Last update: |
5.04.2023 07:08:59 |
Person responsible for content: |
Prof. Dr.-Ing. Kästner, Wolfgang
w.kaestner@hszg.de |
|
Offered in 4 study courses:
| Automation and Mechatronics (B.Eng.) valid from class 2018 |
Automation and Mechatronics (B.Eng.) valid from class 2021 |
Electrical Engineering (B.Eng.) valid from class 2024 |
Mechatronics (M.Eng.) valid from class 2019 |
|
| Semester according to timetable: | WiSe (winter semester)
|
| Module level: | Bachelor/Diplom |
| Duration: | 1 semester |
| Language of Instruction: | English |
| Place where the module will be offered: | Zittau |
|
| ECTS Credits: |
5 |
| Student workload (in hours): |
150 |
|
| Number of hours of teaching: |
total |
subdivided into |
| 4 |
2
Lecture |
1
Seminar/Exercise |
1
Laboratory work |
0
Other |
|
| Self study time (in hours): |
sum |
subdivided into |
| 105 |
70
Preparation of contact hours |
35
Preparation of exam |
0
Other |
|
| Learning and teaching methods: | The methodical aspects of the topic will be communicated by lectures. Seminars and exercises as well as practical courses at laboratory (PC tool) serve for consolidation of knowledge. |
| Further information: |
PC-based exercises will be realized to train the handling of simulation tools. |
| Exam(s) |
| Assessment | Major examination (written report) |
- |
100.0% |
|
| Syllabus plan/Content: |
- foundations of Artificial Neural Networks (ANN)
- application of ANN for modelling and classification
- modelling using Multilayer Perceptron (MLP)
- MLP - structure, demonstration example, software
- classification using on Kohonen Maps (SOM)
- SOM - structure, demonstration example, software
- applications
- simulation tools
|
| Learning Goals |
| Subject-specific skills and competences: |
The students analyze a data base of process and identify the necessity of data preparation. They design data-based models in form of multilayer perceptron as well as classifiers in form of self-organizing maps. They evaluate the quality of designed algorithms based on simulation |
| Generic competences (Personal and key skills): |
The students are able to create and realize strategies for problem solving from the individual point of view or as a result of teamwork.
The students use approaches of system theory. The students evaluate their results and are able to present the results. |
|
| Prerequisites: |
competence from following module (without burden of proof):
- Mathematics |
| Optional: |
competence from module:
- Signals and Systems |
|
| Literature: |
Kruse, R. / Mostaghim, S. / Borgelt, C.: Computational Intelligence. Springer, 2022
Keller, J.: Computational Intelligence. John Wiley & Sons, 2016
Kroll, A.: Computational Intelligence. De Gruyter, 2016
Kruse, R. / Borgelt, C. / Braune, C. / Klawonn, F.: Computational Intelligence. Springer, 2015
Sonnet, D. Neuronale Netze kompakt. Springer, 2022
Ertel, W.: Grundkurs Künstliche Intelligenz. Springer, 2021
Lämmel, U. / Cleve, J.: Künstliche Intelligenz. Carl Hanser, 2020
Beierle, C. / Kern-Isberner, G.: Methoden wissensbasierter Systeme. Springer, 2019
|
