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Introduction
The name “Fuzzy Logic” seems to imply an imprecise methodology that is useful only when accuracy is not necessary
or important. That is what many people assume when they first hear about fuzzy logic—and understandably so. In a
world increasingly manipulated by computers with their absolute “1” or “0” and “on” or “off” concepts, a term like fuzzy
logic suggests inaccuracy or imprecision. Even Webster’s dictionary defines “fuzzy” as:
fuzz·y (–e) adj. 2. not clear, distinct, or precise; blurred
This is not true of fuzzy logic. Fuzzy logic can address complex control problems, such as robotic arm movement, chemical
or manufacturing process control, antiskid braking systems, or automobile transmission control with more precision
and accuracy, in many cases, than traditional control techniques have.
Fuzzy logic was invented and named by Lotfi Zadeh, a professor at the University of California at Berkeley. Fuzzy logic
is a methodology for expressing operational laws of a system in linguistic terms instead of mathematical equations.
Many systems are too complex to model accurately, even with complex mathematical equations, but fuzzy logic’s linguistic
terms provide a useful method for defining the operational characteristics of such a system. These linguistic terms
are most often expressed in the form of logical implications, such as If – Then rules:
If air_temp is WARM, then set fan_speed to MEDIUM.
The terms WARM and MEDIUM are actually sets that define ranges of values known as membership functions. By
choosing a range of values instead of a single discrete value to define the input variable “air_temp”, you can control the
output variable “fan_speed” more precisely. Fuzzy logic controllers can often improve the performance of a control system
by reducing the chance of wild functions in the output that may be caused by variations in the measured input variables.

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