ABSTRACT
Traditional logic theory involves reasoning based on binary sets, which have two valued logic, true or false, yes or no, zero or one. In real life, much of the information that we come across and process is crispy but involves some degree of fuzziness. In general, since knowledge acquisition is difficult and the universe of discourse of each input variable needs to be divided into several intervals, fuzzy logic systems are restricted to fields where expert knowledge is available and the number of input variables is small. Many types of membership functions can be used in fuzzy logic systems, some discrete and some continuous. Commonly used membership functions in fuzzy logic systems include triangular, trapezoidal, Gaussian, bell-shaped, and sigmoidal. A neural network's learning capability provides a good way to adjust expert's knowledge, and it automatically generates additional fuzzy rules and membership functions to meet certain specifications.
