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# What is fuzzy logic?

Aaron Xie

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Fuzzy logic is a model of reasoning that utilizes relative truth values to make decisions and statements. This type of logic is utilized to capture human thinking in machines.

Let’s say we have a machine that analyzes if a tree is tall. With traditional logic, the machine would assign a tree $0$ (not tall) or $1$ (tall) if the tree passes a certain height.

With fuzzy logic, the machine would return an inclusive value between $0$ and $1$ that would determine the relative truth value of the statement.

Unlike traditional logic, the machine utilizing fuzzy logic can say if the tree is extremely tall, somewhat tall, somewhat short, or really short based on its relative value. Fuzzy logic models allow people to use imprecise and vague data to determine partial truths.

For example, people can describe a house as big or small without precise numbers or a dataset. Machines can similarly use fuzzy logic to replicate human thinking.

## How it works

Rules: This is the decision-making system that contains a collection of rules and the if-then conditions developed by experts.

Fuzzification: This step is used to convert crisp inputs, such as data measured by monitors and sensors, into fuzzy datasets.

Inference engine: It determines the rules that will be fired based on the input, and then determines the matching degree of the fuzzy input to each rule being compared. Based on the fired rules, the control actions are determined.

Defuzzification: This step converts the output of fuzzy sets by the inference engine into crisp values.

## How it’s used

Since fuzzy data can make judgments when dealing with imprecise or distorted info, it has considerable potential in various fields. These include:

• Control systems for vehicles, kitchen appliances, etc.

• Image processing

• Route-planning and decision-making

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fuzzy logic
machine learning
logic
inference
ai

CONTRIBUTOR

Aaron Xie