An Etymological Dictionary of Astronomy and Astrophysics
English-French-Persian

1) Of the nature of or resembling → fuzz.
2) Covered with fuzz.
3) Indistinct; → blurred (Dictionary.com).
See also: → fuzzy image, → fuzzy logic, → fuzzy set.

From → fuzz + -y adj. suffix, from O.E. -ig, cognate with L. -icus, → -ic.

Porzvâr "resembling fuzz," from porz, → fuzz, + -var, a suffix of possession, similarity, and aptitude (e.g., omidvâr, sezâvâr, sugvâr, šâhvâr, gušvâr), → -oid.

Same as → blurred image.

→ fuzzy; → image.

A way of → mapping an → input space to an → output space using → fuzzy logic. FIS uses a collection of fuzzy → membership functions and rules, instead of Boolean logic, to reason about data. Also called → fuzzy logic system.

→ fuzzy; → inference; → system.

A process used in a → fuzzy logic system where the → truth value for the premise of each rule is computed and applied to the conclusion part of each rule. This results in one fuzzy set to be assigned to each output variable for each rule.

→ fuzzy; → inference.

A mathematical logic that recognizes more than simple → true and → false → propositions. With fuzzy logic, propositions can be represented with degrees of truthfulness and falsehood. In this system, → truth values are → fuzzy sets without sharp boundaries (→ crisp set) in contrast with → classical logic. Fuzzy logic is applied to a wide range of problems including: industrial control, domestic goods, decision making, robotics, intelligent machines, and image processing in medicine.

→ fuzzy; → logic.

An engineering system which uses → fuzzy logic. It generally consists of four main components: → fuzzification interface (fuzzifier), → fuzzy rule base, → fuzzy inferencing unit, and → defuzzification interface (difuzzifier). Also called → fuzzy inference system.

→ fuzzy; → logic; → system.

A rule base in a → fuzzy logic system constructed to control the → output variable. A fuzzy rule is a simple if-then rule with a condition and a conclusion.

→ fuzzy; → rule; → base.

A set of → truth values in → fuzzy logic that does not have sharp boundaries. Instead, set members have degrees of membership. If the value of 1 is assigned to objects entirely within the set and a 0 is assigned to objects outside of the set, then any object partially in the set will have a value between 0 and 1. This contrast with → crisp sets in → classical logic where members assume a precise value of 1 or 0. Fuzzy sets were first introduced by Lotfi A. Zadeh (1965) and defined as follows. Let X be a space of points, with a generic element of X denoted by x. Thus X = {x}. A fuzzy set A in X is characterized by a → membership function  f_A(x) which associates with each point in X a real number in the interval [0,1], with the values of f_A(x) at x representing the "grade of membership" of x in A. Thus, the nearer the value of f_A(x) to unity, the higher the grade of membership of x in A. Generally, the intersection operations of fuzzy sets are the expansion of that operation on → nonfuzzy sets. In other words, operations on nonfuzzy sets are a particular case of operations on fuzzy sets.

→ fuzzy; → set.

Not → fuzzy. → nonfuzzy set.

→ non-; → fuzzy.

A set that obeys the rules of → classical logic, a → crisp set, as contrasted with a → fuzzy set.

→ nonfuzzy; → set.