# What is Fuzzy Logic?

Date: 15-APR-93 Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth — truth values between “completely true” and “completely false”. It was introduced by Dr. Lotfi Zadeh of UC/Berkeley in the 1960’s as a means to model the uncertainty of natural language. (Note: Lotfi, not Lofti, is the correct spelling of his name.) Zadeh says that rather than regarding fuzzy theory as a single theory, we should regard the process of “fuzzification” as a methodology to generalize ANY specific theory from a crisp (discrete) to a continuous (fuzzy) form (see “extension principle” in [2]). Thus recently researchers have also introduced “fuzzy calculus”, “fuzzy differential equations”, and so on (see [7]). Fuzzy Subsets: Just as there is a strong relationship between Boolean logic and the concept of a subset, there is a similar strong relationship between fuzzy logic and fuzzy subset theory. In classical set theory, a subset U of a set

Related articles: Interview with Lotfi Zadeh, Creator of Fuzzy Logic – by Betty Blair Lotfi Zadeh Reflects on his Youth Commencement Speech – When You Can’t Stop For Lunch – Berkeley, 1997 Fuzzy Logic on the Internet – Mark Hopkins, 1994 Short Biographical Sketch – Lotfi Zadeh Illustration: From Bart Kosko: Fuzzy Thinking: The New Science of Fuzzy Logic, Hyperion, New York 1993. Fuzzy Logic is not what it sounds like. It’s not a nebulous, cloudy, vague way of thinking; in fact, it’s quite the opposite. When anything becomes too complex to fully understand, then it becomes uncertain. The more complex something is, the more inexact or “fuzzier” it will be. Fuzzy Logic provides a very precise approach for dealing with uncertainty which grows out of the complexity of human behavior. The concept was first articulated by Lotfi Zadeh in a paper published in 1965, (“Fuzzy Sets,” Information and Control 8:3, 338-53) which provided the theoretical basis for fuzzy computer chips which appeared 20

Fuzzy logic is a type of mathematics and programming that more accurately represents how the human brain categorizes objects, evaluates conditions, and processes decisions. In the traditional logic system, an item strictly does or does not belong to a group, called a set. That is, an animal either is or is not a dog. Fuzzy logic allows an object to belong to a set to a certain degree or with a certain confidence. Applications of fuzzy logic in contemporary computer systems are too numerous to cite, but they control things like heating mixtures and tooling parts. Our world is incredibly complex, both in breadth and depth. In some ways, it is difficult to adhere to the logical constraints of traditional set theory when describing how we make simple, daily decisions, such as cooking a roast or driving with traffic. Yet we expect computers to make these decisions by simplifying or collapsing complexity and not taking into account uncertainty. Fuzzy logic was invented, and coined, by Dr. Lo

Fuzzy logic is a powerful problem-solving methodology with a myriad of applications in embedded control and information processing. Fuzzy provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. In a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. Unlike classical logic which requires a deep understanding of a system, exact equations, and precise numeric values, Fuzzy logic incorporates an alternative way of thinking, which allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience. Fuzzy Logic allows expressing this knowledge with subjective concepts such as very hot, bright red, and a long time which are mapped into exact numeric ranges. Fuzzy Logic has been gaining increasing acceptance during the past few years. There are over two thousand commercially available products using Fuzzy Logic, ranging f

Fuzzy logic is logic where state membership is, essentially, a float with range 0..1 instead of an int 0 or 1. The mileage you get out of it is that things like, for example, the changes you make in a control system are somewhat naturally more fine-tuned than what you’d get with naive binary logic. An example might be logic that throttles back system activity based on active TCP connections. Say you define “a little bit too many” TCP connections on your machine as 1000 and “a lot too many” as 2000. At any given time, your system has a “too many TCP connections” state from 0 (<= 1000) to 1 (>= 2000), which you can use as a coefficient in applying whatever throttling mechanisms you have available. This is much more forgiving and responsive to system behavior than naive binary logic that only knows how to determine “too many”, and throttle completely, or “not too many”, and not throttle at all.