Linguistics

Fuzzy classification

Philosophy

Biology

Defuzzification

Materials science

Fuzzy set operations

Variable (mathematics)

Data mining

Computer science

Simple (philosophy)

Fuzzy set

Relation (database)

Fuzzy number

Genetics

Nanotechnology

Sequence (biology)

Mathematics

Fuzzy logic

Characterization (materials science)

Epistemology

Type-2 fuzzy sets and systems

Mathematical analysis

Artificial intelligence

IEEE Transactions on Systems Man and Cybernetics

The approach described in this paper represents a substantive departure from the conventional quantitative techniques of system analysis. It has three main distinguishing features: 1) use of so-called ``linguistic'' variables in place of or in addition to numerical variables; 2) characterization of simple relations between variables by fuzzy conditional statements; and 3) characterization of complex relations by fuzzy algorithms. A linguistic variable is defined as a variable whose values are sentences in a natural or artificial language. Thus, if tall, not tall, very tall, very very tall, etc. are values of height, then height is a linguistic variable. Fuzzy conditional statements are expressions of the form IF A THEN B, where A and B have fuzzy meaning, e.g., IF x is small THEN y is large, where small and large are viewed as labels of fuzzy sets. A fuzzy algorithm is an ordered sequence of instructions which may contain fuzzy assignment and conditional statements, e.g., x = very small, IF x is small THEN Y is large. The execution of such instructions is governed by the compositional rule of inference and the rule of the preponderant alternative. By relying on the use of linguistic variables and fuzzy algorithms, the approach provides an approximate and yet effective means of describing the behavior of systems which are too complex or too ill-defined to admit of precise mathematical analysis.

Outline of a New Approach to the Analysis of Complex Systems and Decision Processes