In our daily lives, we constantly deal with things that are not clear-cut or precise. We make decisions based on vague concepts like “warm,” “fast,” or “close.” This type of reasoning, which deals with things that are “not clear or are vague,” is the foundation of fuzzy logic. It’s a way of computing that tries to resemble human decision-making.

The core difference between traditional computing logic and fuzzy logic lies in how they handle truth. Boolean logic, the backbone of most digital computers, is rigid; everything must be either completely true (1) or completely false (0). Think of Boolean logic as a standard light switch: it is either completely ON (1) or completely OFF (0). There is no in-between.

Fuzzy logic, on the other hand, is like a dimmer switch. It can be fully ON (1.0), fully OFF (0.0), or any of the infinite brightness levels in between, like 25% bright (0.25) or 80% bright (0.8). It operates on “degrees of truth,” creating a spectrum that can represent the ambiguity of the real world.

To make this powerful concept work in a computer system, we need a special tool that can translate precise numbers into these “fuzzy” ideas. That tool is the membership function.