Advantages And Disadvantages Of Fuzzy Theory

Uncertainty is the shortage of precise knowledge, incomplete information and uncertain data which describes the state of the environment regardless what is the cause of this insufficient data.1 Different models have been proposed to deal with uncertainty for solving real life problems. 2 The fuzzy theory was introduced by Professor Lotfi Zadeh in 1965 which considers the degree of the membership of elements in a set.3 Zadeh also introduced type 2 fuzzy theory in 1975 which membership grades themselves is fuzzy. 4 In 1983, Intuitionistic fuzzy set theory presented by Attanssov as an extension of the standard fuzzy sets.5 The vague set was first proposed by Gau and Buehrer in 1993. It depends on concept of the degree of membership for truth and the complement of false are not the same.6 The uncertain problems don't need precise models to deal with as it decreases the understanding of the outcome, different applications are deep rooted with uncertain data; the interest of research on effective and efficient methods to deal with uncertainties has increased.7 One of the key problems of artificial intelligence is modeling uncertainty for solving real life problems.8 The characteristics of

The deficient of the precise knowledge that enable reaching a reliable result is uncertainty. Unlike, classical logic supposes that complete and accurate knowledge always subsists 14. Uncertainty affects decision making in unwanted sides while trying to reduce uncertainty risks are accompanied with uncertainty 21.Uncertainty can be represented by three methods which are numeric where a scale with two extreme number from 0 to 100 is used; graphical where gradient bar is used to express expert's opinion in certain events and symbolic where linguistic scale and ranking complemented with numbers can be used 11.This section explores different models for dealing with uncertainty and their