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Information processing for robotics
Informationsverarbeitung in der Robotik
Last Updated: 2026-02-05 15:05:55
Abstract
Short introduction into the information processing methods Neural Networks, Fuzzy Logic, Genetic Algorithms and combination of these methods (hybrid models).In parallel to the presentation of the theoretical background, the application of these methods in robotics are presented, the possibilities and the limits of these methods are discussed.
Objective
The course gives a short introduction into the information processing methods Neural Networks, Fuzzy Logic, Genetic Algorithms and combination of these methods (hybrid models). In parallel to the presentation of the theoretical background, the application of these methods in robotics are presented, the possibilities and the limits of these methods are discussed.
Content
The topics in the course are: 1. Artificial Intelligence (AI) 1.1. Introduction 1.2. History of AI, lessons learned from the beginnings of AI 2. Neural Networks 2.1. Introduction in Neural Networks 2.1. Perceptrons, Multilayer Perceptrons 2.2. Kohonen's Self Organizing Maps and extensions of H. Ritter 2.3. RuleNet 2.4. Application examples in robotics with these models 3. Fuzzy Logic 3.1 Introduction 3.2 Theory and application examples in robotics 4. Neuro-Fuzzy Systems 5. Genetic Algorithms 5.1 Introduction 5.2 Theory and application examples in robotics 5.3 Genetic Programming 5.4 Combination with Neural Networks
Resources
Lecture Notes
Copies of the slides will be distributed during the course.
Literature
References to papers and books will be presented during the lectures.
General Information
- Language
- German
- Levels
- BSC , DS , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 30 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Informationsverarbeitung in der Robotik |
|
3 h weekly |