Fuzzy Systems
| Title | ΑΣΑΦΗ ΣΥΣΤΗΜΑΤΑ / Fuzzy Systems |
| Code | ΗΚ0601 |
| Faculty | Engineering |
| School | Electrical and Computer Engineering |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Ioannis Theocharis |
| Common | No |
| Status | Active |
| Course ID | 20000552 |
| Academic Year | 2017 – 2018 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600111409
|
Class Schedule
| Building | Πολυτεχνείο (πτέρυγα Γ) |
| Floor | Όροφος 1 |
| Hall | Α2 (3) |
| Calendar | Monday 11:00 to 14:00 |
Course Type 2016-2020
- General Knowledge
- Skills Development
Course Type 2011-2015
General Foundation
Mode of Delivery
- Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
The learning objectives are designated so that after the completion of this course, the student should accomplish the following:
• Assimilate the notions and principles of the different parts comprising fuzzy systems: linguistic descriptions, representation and reasoning of fuzzy IF/THEN rules, fuzzy inference methodologies, and decision making.
• For a given problem under consideration, he should be able to construct the fuzzy rule base, select the appropriate fuzzy implication operator, and the respective fuzzification/defuzzification strategies.
• In regard to fuzzy logic controllers (FLCs), he should be able to develop the proper FLC for a controlled system at hand, define the fuzzy rule base of control rules, and follow the correct procedure for gain tuning to achieve the optimal system’s response.
• In the context of fuzzy modeling for system’s identification and time-series prediction, he should be able to design a descriptive fuzzy model, implement the learning algorithms for the neuron-fuzzy network’s weights, and extract the linguistic fuzzy rules describing the model’s behavior.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in an interdisciplinary team
- Generate new research ideas
Course Content (Syllabus)
• Basic notions and terminology of fuzzy sets, properties and operations of fuzzy systems, membership functions. Resolution and extension theorems. A-cuts, intersection, union and complement of fuzzy sets, parameterized operators.
• Fuzzy relations, properties and operations of fuzzy relations, fuzzy relation composition, composition operators, fuzzy set-relation composition.
• Fuzzy numbers and their representation, fuzzy arithmetic. Linguistic variables, fuzzy logic principles.
• Fuzzy IF/THEN rules, Implication operators, compositional rules of inference, fuzzy rule bases, fuzzy inference methods.
• Structure of fuzzy systems, premise part partition, fuzzification/defuzzifications forms.
• Fuzzy logic controllers(FLC), FLC structures, PI, PD and PID types of FLC. Variables normalization, design of FZ-PI, FZ-PD FLCs. Three term FLCs FZ-PID. Design and tuning of FLC gains, experimental results of FLCs.
• TSK fuzzy models with crisp and polynomial rule outputs, neuron-fuzzy representation, learning algorithms and training of fuzzy models.
Keywords
Fuzzy sets, fuzzy logic, fuzzy re3lations, fuzzy IF/THEN rule, compositional rules of inference, fuzzy controllers/models
Educational Material Types
- Notes
- Slide presentations
- Interactive excersises
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
Description
- Presentation of illustrative example including fuzzy rule bases, fuzzy logic controllers, fuzzy models yusing the MATLAB software package.
- Preparation of web page at e-THMMY presenting: the course material,anouncements, projects assignments, and communication with students.
- Lectures are given via Powerpoint presentations, and usage of the local network of THMMY.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 30 | ✓ | |||
| Tutorial | 9 | ✓ | |||
| Written assigments | 64 | ✓ | |||
| Total | 103 |
Student Assessment
Description
- Written exams on the entire material of the course, including problems resolution and short answer questions.
- Preparation of a set Written reports (4-5) on several important issues of the material.
- Most often, oral examination on the student projects.
- The final grade in the course is determnined as a weighted average between the above items.
- The evaluation criteria are presented behorehand at the corse site e-THMMY.
Student Assessment methods
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1) «Υπολογιστική Νοημοσύνη και Εφαρμογές», Ι. Μπούταλης, Γ. Συρακούλης, Εκδόσεις: Γ. ΣΥΡΑΚΟΥΛΗΣ, 2010, ISBN: 978-960-93-2008-5
2) Εισαγωγή στην Ασαφή Λογική (Fuzzy Logic)», Γ. Θεοδώρου, Εκδόσεις: ΤΖΙΟΛΑ, 2010, ISBN: 978-960-418-218-3.
Additional bibliography for study
• Δίνονται εκτεταμένες διδακτικές σημειώσεις από τον διδάσκοντα οι οποίες καλύπτουν ικανοποιητικά τις αρχές ανάλυσης ασαφών συστημάτων καθώς και τις μεθοδολογίες σχεδίασης ασαφών ελεγκτών και μοντέλων. Επίσης, διατίθενται οι διαλέξεις του διδάσκοντα σε μορφή Power Point.
• Δίνονται λίστες εργασιών τις οποία οι φοιτητές εκπονούν προαιρετικά κατ’ οίκον. Οι εργασίες οργανώνονται σε τέσσερις βασικές κατηγορίες γνωστικού περιεχομένου, κάθε μία από τις οποίες επικεντρώνεται στα σημαντικότερα αντικείμενα του μαθήματος.
• Δίνεται τέλος, λίστα ξενόγλωσσων συγγραμμάτων από την πλούσια Αγγλική κυρίως βιβλιογραφία, παρόλο που η διανομή στους φοιτητές τέτοιων συγγραμμάτων δεν είναι εφικτή στην τρέχουσα φάση. Η παραπάνω λίστα αναρτάται στην ιστοσελίδα του μαθήματος στο e-THMMY.
1) R. R. Yager and D. P. Filev, Essentials of fuzzy modeling and control, John Willey & Sons, Inc., 1994.
2) L. H. Tsoukalas and R. E. Uhrig, Fuzzy and Neural approaches in Engineering, John Willey & Sons, Inc., 1997.
3) J.-S. R. Yang, C.-T. Sun, and E. Mizutani, Neuro-fuzzy and Soft Computing. A computational approach to learning and machine intelligence, Prentice Hall, NJ, 1997.
4) C.-T. Lin and C. S. G. Lee, Neural Fuzzy Systems, Prentice Hall, Inc., 1997.
5) Bart Kosko, Fuzzy engineering, Prentice Hall, Inc., 1997.
6) Li-Xin Wang, A course in fuzzy systems and control, Prentice Hall, 1997.
7) D. Driakov, H. Hellendroorn, and M. Reinfrank, An introduction to fuzzy control, Springer-Verlag, 1996.
8) M. Jamshidi, N. Vadiee, and T. Ross, Fuzzy logic and control Sostware and hardware applications, PTR Prentice Hall, Englewood Cliffs, NJ.
Last Update
17-07-2013
