in mathematics, a form of logic based on the concept of a fuzzy set. Membership in fuzzy sets is expressed in degrees of truth—i.e., as a continuum of values ranging from 0 to 1. In a narrow sense, the term fuzzy logic refers to a system of approximate reasoning, but its widest meaning is usually identified with a mathematical theory of classes with unclear, or “fuzzy,” boundaries. Control systems based on fuzzy logic are used in many consumer electronic devices in order to make fine adjustments to changes in the environment. Fuzzy logic concepts and techniques have also been profitably used in linguistics, the behavioral sciences, the diagnosis of certain diseases, and even stock market analysis.

Most concepts used in everyday language, such as “high temperature,” “round face,” or “aquatic animal,” are not clearly defined. In 1965 Lotfi Zadeh, an engineering professor at the University of California at Berkeley, proposed a mathematical definition of those classes that lack precisely defined criteria of membership. Zadeh called them fuzzy sets. Membership in a fuzzy set may be indicated by any number from 0 to 1, representing a range from “definitely not in the set” through “partially in the set” to “completely in the set.” For example, at age 45 a man is neither very young nor very old. This makes it difficult in traditional logic (see laws of thought (thought, laws of)) to say whether or not he belongs to the set of “old persons.” Clearly he is “sort of” old, a qualitative assessment that can be quantified by assigning a value, or degree of membership, between 0 and 1—say 0.30—for his inclusion in a fuzzy set of old persons.

Fuzzy sets are a generalization of ordinary sets, and they may be combined by operations similar to set union, intersection, and complement. However, some properties of ordinary set operations are no longer valid for fuzzy sets. For instance, the intersection of a fuzzy subset and its complement may be nonempty. In a logic based on fuzzy sets, the principle of the excluded middle is therefore invalid.

Fuzziness as defined by Zadeh is nonstatistical in nature—it represents vagueness due to human intuition, not uncertainty in the probabilistic sense. Membership in a fuzzy set is usually represented graphically. Membership functions are determined by both theoretical and empirical methods that depend on the particular application, and they may include the use of learning and optimization techniques such as neural networks (neural network) or genetic algorithms (see artificial intelligence: Evolutionary computing (artificial intelligence)).

In technical applications, fuzzy control refers to programs or algorithms using fuzzy logic to allow machines to make decisions based on the practical knowledge of a human operator. The fundamental problem of automatic control is that of determining the appropriate response of the system, or production plant, for any given set of conditions. Conventional control techniques are based on explicit mathematical descriptions of the system, typically a set of differential equations (differential equation) involving a small number of variables. Fuzzy control, on the other hand, does not require an exact theoretical model but only the empirical knowledge of an experienced operator. This knowledge is then expressed as a set of linguistic rules of the form “if 【present conditions】, then 【action to be taken】.” For example, “if temperature is low and image density is high, then electric charge should be medium” is one of nine heuristic rules governing the smooth operation of a photocopier. The ambiguous terms—low temperature and high density—are represented as fuzzy sets, and the various linguistic rules are represented as mathematical relations between these sets. The control strategy can then be encoded as an algorithm or computer program. During the operation of the machine, sensors measure the current values of the input variables (temperature and image density, in this case), and a computer or electronic chip then determines the appropriate values of the action variables (e.g., electric charge).

E.H. Mamdani, while a lecturer at Queen Mary College, London, working in the design of learning systems, is credited with implementing the first fuzzy logic controller in the early 1970s. Mamdani and his student Seto Assilian wrote down 24 heuristic rules for controlling the operation of a small steam engine and boiler combination. They then used fuzzy sets to translate these linguistic rules into an algorithm that successfully controlled the system, thus demonstrating the power of the new approach.

Commercial applications of fuzzy logic began to appear in the early 1980s, particularly in Japan, which soon became the centre of academic and industrial research on fuzzy systems. For example, fuzzy logic has been used in the control of cement manufacture and water purification processes, and a fuzzy controller designed by engineers from Hitachi, Ltd., was used to operate the automatic subway trains of the Japanese city of Sendai. Throughout the decade, Japanese consumers were offered scores of goods featuring fuzzy logic components. These included television sets that adjusted volume and contrast depending on noise level and lighting conditions; “smart” washing machines that selected the optimal washing cycle on the basis of quantity and quality of dirt and load size; fuzzy microwave ovens and rice cookers that adjusted for humidity; and video cameras with fuzzy chips that properly adjusted focus and lighting with several objects in the picture. For marketing purposes, the term fuzzy was presented as synonymous with “efficient operation requiring minimal human intervention.”

The Japanese frenzy for fuzzy products eventually subsided, but fuzzy logic is still very much present, if less conspicuously, in a number of consumer products. The automatic transmissions of certain automobiles, for instance, contain a fuzzy component that senses driving style and engine load so as to select the best gear.

Practical applications of fuzzy logic are not restricted to engineering and related fields. In medicine, expert systems (expert system) using fuzzy inference can help doctors diagnose diabetes and prostate cancer. Management science, stock market analysis, information retrieval, linguistics, and behavioral sciences are just a few of the other domains where fuzzy logic concepts and techniques have been profitably used.

The late 1990s witnessed the development of hybrid systems, which combine the advantages of two or more computing techniques. So-called neuro-fuzzy systems integrate fuzzy logic and artificial neural networks, enabling a certain form of learning. Systems with neuro-fuzzy components may be found in fields such as stock market prediction, intelligent information systems (information system), and data mining (see database).