Fuzzy Logic refers to a logic system which represents knowledge and reasons in an imprecise or fuzzy manner for reasoning under uncertainty. Unlike the classical logical systems, it aims at modeling the imprecise modes of reasoning that play an essential role in the human ability to infer an approximate answer to a question based on a store of knowledge that is inexact, incomplete, or not totally reliable. It is usually appropriate to use fuzzy logic systems when a mathematical model of a process does not exist, or does exist but is too difficult to encode and too complex to be evaluated fast enough for real time operation. A fundamental element of fuzzy logic is the membership function which describes the degree of a certain variable “x”, belonging to a fuzzy set “A”. This degree of membership is expressed by a number between
0 and 1 in which a membership value of 1 means that the
variable is completely satisfactory for the fuzzy set “A”, whereas a value of 0 means that it is completely unacceptable in that fuzzy set, and it does not belong to the set “A” at all. Any deviation is acceptable with an intermediate degree of satisfaction between 0 and 1. A fuzzy set can be defined by a function called the membership functions.