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Fuzzy Logic
Logic that allows degrees of truth (not just True/False). Models uncertainty with values between 0 and 1.
graph TD
A["Is it Cold?"] --> B["Crisp Logic<br/>Yes = 1<br/>No = 0"]
A --> C["Fuzzy Logic<br/>Maybe Cold = 0.3<br/>Not really cold = 0.7"]
Useful in control systems and decision-making under vagueness.
Still used in various use cases to find out similarity like New Jersey similar to Jersey.
Pros
- Handles imprecise, uncertain, or linguistic data (“high temperature”, “low risk”).
- Good for rule-based control.
Cons
- Not data-driven → rules must be defined manually.
- Limited learning ability compared to ML.
Use Cases
- Washing machines that adjust cycles based on “fuzziness” of dirt level.
- Air conditioning systems adapting to “comfort level”.
- Automotive control (braking, transmission).
- Risk assessment systems.