This course aims at introducing the fundamental theory and concepts of computational intelligence methods, in particular neural networks, fuzzy systems, genetic algorithms and their applications in the area of machine intelligence. This can be summarized as: (1) To understand the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate reasoning, fuzzy inference systems, and fuzzy logic control and other machine intelligence applications of fuzzy logic. (2) To understand the basics of an evolutionary computing paradigm known as genetic algorithms and its application to engineering optimization problems. (3) To understand the fundamental theory and concepts of neural networks, neuro-modeling, several neural network paradigms and its applications. Contents: Introduction to Fuzzy Logic. Introduction to Fuzzy Sets. Introduction to Fuzzy Inference Systems. Fuzzy Logic Applications. Introduction to Genetic Algorithm. Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures.
| AUs | 3.0 AUs |
| Grade Type | |
| Prerequisite | |
| Not Available To Programme | |
| Not Available To All Programme With | |
| Not Available As BDE/UE To Programme | |
| Not Available As Core To Programme | |
| Not Available As PE To Programme | |
| Mutually Exclusive With | |
| Not Offered As BDE | |
| Not Offered As Unrestricted Elective | |
| Exam |
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 930 | |||||
| 1000 | |||||
| 1030 | |||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | |||||
| 1500 | |||||
| 1530 | |||||
| 1600 | |||||
| 1630 | |||||
| 1700 | |||||
| 1730 | |||||
| 1800 |
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