A General Study on Genetic Fuzzy Systems

As it is known, a rule based system (production rule system) has been successfully used to model human problem-solving activity and adaptive behavior, where a classic way to represent the human knowledge is the use of IF/THEN rules. The satisfaction of the rule antecedents gives rise to the execution of the consequent, one action is performed. The conventional approaches to knowledge representation are based on bivalent logic. A serious shortcoming of such approaches is their inability to come to grips with the issue of uncertainty and imprecision. As a consequence, the conventional approaches do not provide an adequate model for modes of reasoning and all commonsense reasoning fall into this category. Fuzzy Logic (FL) may be viewed as an extension of classical logical systems, provides an eeective conceptual framework for dealing with the problem of knowledge representation in an environment of uncertainty and imprecision. FL, as its name suggests, is the logic underlying modes of reasoning which are approximate rather than exact. The importance of FL derives from the fact that most modes of human reasoning-and especially commonsense reasoning-are approximate in nature. FL is concerned in the main with imprecision and approximate reasoning. The applications of FL to rule based systems have been widely developped. From a very broad point of view a Fuzzy System (FS) is any Fuzzy Logic Based Sytems, where FL can be used either as the basis for the representation of diierent forms of knowledge systems, or to model the interactions and relationships among the system variables. FS have been shown to be an important tool for modelling complex systems, in which, due to the complexity or the imprecision, classical tools are unsuccessful.