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Fuzzy logic is a logic operations method based on many-valued logic rather than binary logic (two-valued logic). Two-valued logic often considers 0 to be false and 1 to be true. However, fuzzy logic deals with truth values between 0 and 1, and these values are considered as intensity (degrees) of truth.
Fuzzy logic may be applied to many fields, including control systems, neural networks and artificial intelligence (AI).
Fuzzy logic can be used to describe how information is processed inside human brains. For example, it can be argued that humans do not know the difference between fat and thin. Five people may be fat and not have the same severity of fatness. Or, one person may appear thin, compared to another, while both are actually fat. Using fuzzy logic, you can assign different logic values for fatness, ranging from 0 to 1, according to severity of fatness.
Variables between the extremes of zero and one are closer to the concept of probability, which means there is a major correlation between the science of probability and fuzzy logic. However, fuzzy logic refers to intensity of truth, while probability refers to likelihood.
Lotfi Zadeh is credited with fuzzy logic’s formulation, which he developed while working for the University of California in the 1960s. Zadeh’s research focused on implementing methods that allowed computers to understand human language, such as multiple degrees of attributes that cannot be described in terms of zeroes and ones.