In the past several years, significant theoretical results have been achieved in the field of fuzzy logic systems. Wang and Mendel (1992c) [48] and Kosko (1992) [21]showed that fuzzy logic systems are universal approximators, which means that a fuzzy logic system `can uniformly approximate any real continuous nonlinear function to an arbitrary degree of accuracy' (Mendel, 1995 [25]). This existence theorem shows us above all the possibilities offered by fuzzy logic, but does not indicate the manner in which to create fuzzy logic systems. In other words, the number of inputs, the number of fuzzy sets used to describe fuzzy variables, and the number of rules essentially influence the quality of the solution generated by a fuzzy logic system. It should be emphasized that feed-forward neural networks and fuzzy logic systems are techniques that can be used to solve the same class of problems. Since fuzzy logic system parameters can be initialized using expert knowledge, while weights in feed-forward neural networks are most often initialized randomly, fuzzy logic systems are tuned much more quickly than the tuning of feed-forward neural networks.