🧠 EE4285 Computational Intelligence
![Fuzzy logic and systems](/notes/ee4285-computational-intelligence/featured_hu5f159b7ea8aa422fc680314a50d3c580_125728_dda753d879fe1467571cc7fae4f47ec3.webp)
The courses provides an introduction to fundamental theory and concepts of computational intelligence methods, specifically, fuzzy systems, neural networks, genetic algorithms and their applications in the area of machine intelligence.
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Lecture 1: Introduction to Fuzzy Sets
A fuzzy set is totally characterised by a Membership function (MF). And is mathematically expressed as $$S = {(x, \mu_s(x) | x\in X)}$$ The above equation is known as characteristic equation.
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Lecture 2: Fuzzy Relations & Compositions
Fuzzy Relations Binary relation: When there are two universe of discourse $X \times Y$ , then a fuzzy relation is defined as $$R = {((x, y), \mu_{R}(x,y))| (x,y) \in X \times Y}$$
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Lecture 3: Fuzzy If-then & Fuzzy Reasoning
Fuzzy If-then rule Fuzzy If-then rules basically encodes fuzzy relations. If $x$ is $A$, then $y$ is $B$. In $A$ and $B$ are linguistic values defined by fuzzy sets on universes of discourse $X$ and $Y$.
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Lecture 4: Fuzzy Inference System
General definition: A computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. Interpretation: Specifically, it is a framework used to model and infer the behaviour of a system.
References
- Dr. Su Rong. 2022. “EE4285: Computational Intelligence,” Singapore: Nanyang Technological University.