inf303 - Fuzzy Control and Artificial Neural Networks in Robotics and Automation (Complete module description)

Module label	Fuzzy Control and Artificial Neural Networks in Robotics and Automation
Module code	inf303
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Computing Science
Applicability of the module	Master's Programme Computing Science (Master) > Angewandte Informatik Master's Programme Computing Science (Master) > Technische Informatik Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Responsible persons	Fatikow, Sergej (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites
Skills to be acquired in this module	Experts in different branches try to approach their application-specific control and information processing problems by using fuzzy logic and artificial neural networks (ANN). The experiences gathered up to now prove robotics and automation technology to be predestined fields of application of both these approaches. The major topics of the course are control problems in robotics and automation technology, principles of fuzzy logic and ANN and their practical appplications, comparison of conventional and advanced control methods, combination of fuzzy logic and ANN in control systems. The course gives a comprehensive treatment of these advanced approaches for interested students. Professional competence The students: recognise control problems in robotics and automation technology, name principles of fuzzy logic and ANN and their practical appplications, compare conventional and advanced control methods, - characterise the combination of fuzzy logic and ANN in control systems Methodological competence The students: will acquire knowledge of the tools, methods and applications in fuzzy logic and ANN deepen their knowledge for the practical use of the given methods can use common software tools for design and application of fuzzy logic and ANN Social competence The students: gain experience in interdisciplinary work are integrated into the recent research work Objective of the module / skills: Self-competence The students: are able to transfer the gained knowledge for later use in their theses or studies for AMiR can Design (complex) fuzzy logic controller and ANN systems reflect their (control) solutions by using methods learned in this course
Module contents	Control problems in robotics and automation technology Basic ideas of fuzzy logic and ANN Principles of fuzzy logic Fuzzy logic of rule-based systems ANN models ANN learning rules Multilayer perceptron networks and backpropagation Associative networks Self-organizing feature maps PID design principles Design of fuzzy control systems Fuzzy logic application examples Design of ANN control systems ANN application examples Fuzzy + Neuro: principles and applications
Recommended reading	Lecture notes will be provided, to prepare for oral examination
Links
Language of instruction	German
Duration (semesters)	1 Semester
Module frequency	annual
Module capacity	unlimited
Teaching/Learning method	V+Ü

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	3	SuSe	42
Exercises	1	SuSe	14
Total module attendance time			56 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	At the end of the lecture period until the beginning of the next semester	Hands-on-exercises and oral Exam