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 research 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. Self Organized Features Maps capabilities to cluster inputs using self adoption techniques have been very useful in generating fuzzy membership functions for the subsets of the fuzzy variables. Then the Parallel Genetic Algorithms use these membership functions along with the training data set to find the most fit fuzzy rule set from a number of initial sub populations according to the fitness function. The illustrations of the proposed model and its sub modules along with the experimental results and comparisons with previous techniques in generating rules from data sets are declared.

Abstract-Handling uncertain knowledge is a very tricky problem in the current world as the data, we deal with, is uncertain, incomplete and even inconsistent. Finding an efficient intelligent framework for this kind of knowledge is a challenging task. The knowledge based framework can be represented by a rule based system that depends on a set of rules which deal with uncertainness in the data. Fuzzy rough rules are a good competitive in dealing with the uncertain cases. They are consisted of fuzzy rough variables in both the propositions and consequences. The fuzzy rough variables represent the lower and upper approximations of the subsets of a fuzzy variable. These fuzzy variables use labels (fuzzy subsets) instead of values. An efficient fuzzy rough rule based system must depend on good and accurate rules. This system needs to be enhanced to view the future recommendations or in other words the system in time sequence. This paper tries to make a rule based system for uncertain knowledge using fuzzy rough theory to generate the desired accurate rules and then use fuzzy cellular automata parallel system to enhance the rule based system developed and find out what the system would look like in time sequence so as to give good recommendations about the system in the future. The proposed model along with experimental results and simulations of the rule based systems of different data sets in time sequence is illustrated.