Artificial intelligence and knowledge representation techniques. introduction to fuzzy sets. Basic operations on fuzzy sets. Variable domains. Linguistic rules. Defuzzification process. Fuzzy controller: preliminaries & basic construction. The Approach of Mamdani. The approach of Takagi and Sugeno Design parameters of fuzzy controllers - Principles of neural networks - Learning methods - Neural networks for control and modeling - Neuro fuzzy control systems - application studies on mechanical systems.
M.Sc. in Mechanical Engineering
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| content serial | Description |
|---|
| 1 | Introduction to artificial intelligence systems |
| 2 | Introduction to fuzzy logic fuzzy sets. |
| 3 | Fuzzy relation properties, Membership s determination |
| 4 | Defuzzification process. Fuzzy controller: preliminaries & basic construction. |
| 5 | The Approach of Mamdani. The approach of Takagi and Sugeno |
| 6 | Design parameters of fuzzy controllers |
| 7 | Case study using fuzzy logic control |
| 8 | Case study using fuzzy logic control |
| 9 | Principles of neural networks |
| 10 | Learning methods |
| 11 | Neural networks for control and modeling |
| 12 | Neuro -fuzzy control systems ANFIS |
| 13 | Case study using ANN |
| 14 | Case study using ANN |
| 15 | Case study using ANFIS |
| 16 | Final Examination |
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