- Appropriate modeling of linguistic terms by appropriate fuzzy sets (K6)
- Knowing and proving properties of t-norms, t-conorms, negation and implcations (K3, K4)
- Developing a rule base for an application setting (K6)
- Knowing and explaining different fuzzy inference schemata (K2, K3)
- Designing and executing exemplary Mamdani und Tagaki-Sugeno-Kang fuzzy systems (K1, K5, K6)
- Proving mathematical properties of fuzzy systems (K1, K2)
- Knowing and applying clusteringtechniques for obtaining fuzzy sets and a rule base along with their evaluation (K1, K3, K5)
- Analyzing, interpreting and developing strategies for further evolving given examples of data based fuzzy systems (K4, K5, K6)
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- Types of fuzzy sets (triangular, trapezoidal, Gaussian)
- Semantic models for many-valued conjunction, disjunction, negation and implication
- Fuzzy inference schemes (assignment, deductive approach)
- Mamdani und Tagaki-Sugeno-Kang fuzzy
- Selected defuzzification strategies
- Clustering techniques like fuzzy c-means, Gustafson-Kessel models for determining data based fuzzy systems
- Evolving fuzzy systems: strategies for local and global parameter updating and for structure evolving
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