Module Handbook

  • Dynamischer Default-Fachbereich geändert auf EIT

Course EIT-AUT-453-K-7

Methods of Soft Control (2V, 3.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
2 V Lecture 3.0 CP 28 h 62 h
(2V) 3.0 CP 28 h 62 h

Basedata

SWS 2V
CP, Effort 3.0 CP = 90 h
Position of the semester 1 Sem. in WiSe
Level [7] Master (Advanced)
Language [EN] English
Lecturers
Area of study [EIT-AUT] Automation Control
Livecycle-State [NORM] Active

Contents

  • Fuzzy control: fuzzy sets and operators, membership function, fuzzification, fuzzy implication, defuzzification, basic working principle of fuzzy controllers, typical applications.
  • Artificial intelligence and its application to control, modelling and diagnosis: supervised learning, unsupervised learning, Back-Propagation algorithm, radial basis function network, self-organizing map, support vector machine, reinforcement learning, training and validation of artificial neural networks, typical application scenarios.
  • Genetic algorithms and evolutionary algorithms: stochastic optimization approaches, selection of parameters, application in optimization, application in modelling, control and diagnosis.

Literature

  • S. Haykin. Neural networks and learning machines. Pearson, 2016.
  • E. Trillas, L. Eciolaza. Fuzzy Logic: An Introductory Course for Engineering Students. Springer, 2015.
  • K.F. Man, K.S. Tang, S. Kwong. Genetic Algorithms: Concepts and Designs. Springer, 2012.
  • J. Adamy. Fuzzy Logic, Neuronale Netze und Evolutionäre Algorithmen (in German). Shaker Verlag, 2015.

Materials

Blackboard, slides (PowerPoint, PDF), lecture-specific website, exercises in MATLAB/Simulink

Requirements for attendance (informal)

Modules:

Requirements for attendance (formal)

None

References to Course [EIT-AUT-453-K-7]

Module Name Context
[EIT-AUT-453-M-7] Methods of Soft Control P: Obligatory 2V, 3.0 LP