Fuzzy logic is a mathematical and programming approach that allows objects to belong to a set to some extent. It is used in computer systems to make decisions and control machines. Fuzzy logic is not vague or provisional, but a practical way to teach computers how to make decisions. It allows for middle ground and solves problems that a simplistic logic system cannot. The output is always clear and not fuzzy.
Fuzzy logic is a type of mathematics and programming that more accurately represents how the human brain classifies objects, evaluates conditions, and makes decisions. In the traditional logical system, an element that belongs or does not strictly belong to a group is called a set. For example, an animal is or is not a dog. Fuzzy logic allows an object to belong to a set to some extent or with some certainty. Applications of fuzzy logic in contemporary computer systems are too numerous to mention, but they control things like the heating of mixtures and tool parts.
The world is incredibly complex, both in breadth and depth. In some ways, it’s hard to adhere to the logical constraints of traditional set theory when describing how simple, everyday decisions are made, like cooking a roast or driving in traffic. However, computers are expected to make these decisions by simplifying or reducing complexity and disregarding uncertainty. Fuzzy logic was invented and coined by Dr. Lotfi Zadeh at UC Berkeley in 1965, when he was thinking about mathematics, linguistics and common sense.
To understand how fuzzy logic is not a vague and provisional system, but can be used in a very practical way to teach computers how to make decisions, an example can be useful. Building on the “No dogs in the house” rule, this logically means that IF the object is a dog, THEN it must not be in the house. Somehow, it can be inferred that a stuffed animal that looks like a Dalmatian will be allowed in, but a real live Dalmatian will not. However, some questions may remain, such as whether dogs for the blind can be allowed or whether animals that are half Husky and half wolf are allowed inside.
Fuzzy logic allows for these middle ground when it comes to fulfilling requirements and initializing consequences. Instead of an animal absolutely belonging to the set of dogs, it can belong to some extent. A golden retriever might have an associated value of 1.0, as close as you can get to a “fully” dog, while a chihuahua might have 0.8, due to its size. A blind dog might have a value of only 0.4, as it is often allowed where other dogs are not allowed.
This flexible system solves problems and controls machines that a simplistic logic system could not. The output, or decision, is always clear and not fuzzy; in other words, the output is always “sharp”. Eventually, the dog is either indoors or out on the porch – it’s never halfway there. That’s why “fuzzy” doesn’t mean uncertain or unknown.
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