A Hybrid of Self Organized Feature Maps and Parallel Genetic Algorithms for Uncertain Knowledge
The need to handle uncertainty and vagueness in real world becomes a necessity for developing good and efficient systems. Fuzzy rules and their usage in fuzzy systems help too much in solving these problems away from the complications of probability mathematical calculations. Fuzzy rules deals will words and labels instead of values of the variables. These labels are called variable's subsets and needed to be prepared carefully to make sure that the fuzzy rules depend on accurate propositions. This paper tries to design an efficient set of rules that is used later for inference by a hybrid model of Self Organized Features Maps and Parallel Genetic Algorithms.