Fuzzy logics are used to interpret vague, ambiguous, or imprecise information. Their decision processes are similar to human decisions to interpret exact solutions from approximate data.
For example, when interpreting digital signals, a fuzzy logic can interpret intermediate values in addition to the two states "0" and "1". This third logic value may lie exactly between the two binary values "0" and "1" and be interpreted by the fuzzy logic as "possible". Furthermore, the levels between the two logic states can be divided into several intervals and conceivable probability values can be assigned to each interval step. This means that the level closest to the logical "1", for example, also represents the highest probability.
With its probability assignment, the fuzzy technique representing fuzziness offers significant advantages in the control and optimization of critical processes, for example in automation and process control. Furthermore, fuzzy logic is also used in expert systems in knowledge processing.