Stud.IP Uni Oldenburg
University of Oldenburg
04.12.2021 03:43:37
inf303 - Fuzzy Control and Artificial Neural Networks in Robotics and Automation (Course overview)
Department of Computing Science 6 KP
Module components Semester courses Sommersemester 2017 Examination
Lecture
  • No access 2.01.303 - Fuzzy-Regelung und künstliche neuronale Netze in Robotik und Automation Show lecturers
    • Prof. Dr. Sergej Fatikow
    • Tobias Tiemerding, M. Sc.
    • Marius Knaust

    Monday: 14:00 - 16:00, weekly (from 03/04/17), Location: A05 1-160
    Thursday: 14:00 - 16:00, weekly (from 06/04/17), V/Ü, Location: A04 2-221
    Dates on Monday. 07.08.17 10:45 - 12:45, Monday. 07.08.17 11:45 - 12:30, Tuesday. 08.08.17 09:30 - 11:45, Tuesday. 08.08.17 09:30 - 1 ...(more), Location: ((A1-3-310))

Exercises
Notes for the module
Time of examination
At the end of the lecture period until the beginning of the next semester
Module examination
Hands-on-exercises and oral Exam
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