SylabUZ
| Course name | Intelligent control methods |
| Course ID | 11.9-WE-AutD-IntelConMeth.-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | WIEiA - oferta ERASMUS / Automatic Control and Robotics |
| Education profile | - |
| Level of studies | Second-cycle Erasmus programme |
| Beginning semester | winter term 2018/2019 |
| Semester | 2 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
Introduction to artificial neural networks and fuzzy logic.
Shaping skills in design fuzzy and neural network-based control systems
Control theory
Introduction to neural networks: properties, essential topologies and connections, learning methods, application perspectives in control engineering and robotics.
Multilayer feedforward networks: design of an essential processing unit. Network structures and working rules, backpropagation algorithm and its modifications, knowledge generalization, regularization. Neural networks in classification tasks. Dynamic neural networks: feedforward networks with delay, recurrent networks (Williams-Zipser network), partially recurrent network (Elman network). Serial and parallel models in system identification. Essential control structures using neural networks.
Introduction to fuzzy logic: fuzzy sets, fuzzification and defazification. Rule base and its generation. Fuzzy inference models: Mamdani and Takagi-Sugeno. Design of Takagi-Sugeno models. Design of fuzzy PID. State feedback controller with Takagi-Sugeno models.
Lecture: conventional lecture
Lab: laboratory exercises
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture - positive score of a written exam
Lab – positive scores concerning all laboratory tasks
Final score composition = Lecture: 50% + Lab: 50%
1. Korbicz, A. Obuchowicz, D. Uciński D., Sieci neuronowe. Podstawy i zastosowania, Akademicka Oficyna Wydawnicza, PLJ, Warszawa, 1994
2. R. Rojek, K. Bartecki, J. Korniak, Zastosowanie sztucznych sieci neuronowych i logiki rozmytej w automatyce, Oficyna Wydawnicza Politechniki Opolskiej, Opole, 2000
3. R.R. Yager, D.P. Filev, Podstawy modelowania i sterowania rozmytego, WNT, Warszawa, 1995
4. M. Noorgard, O. Ravn, N.M. Poulsen, L.K. Hansen, Neural networks for Modelling and Control of Dynamic Systems, Springer-Verlag, Londyn, 2000
Modified by dr hab. inż. Wojciech Paszke, prof. UZ (last modification: 01-05-2020 17:10)
