TITLE

A Hybrid of Self Organized Feature Maps and Parallel Genetic Algorithms for Uncertain Knowledge

AUTHOR(S)
Gamal, Mona; Abo El-Fatoh, Ahmed; Barakat, Shereef; Radwan, Elsayed
PUB. DATE
December 2012
SOURCE
International Journal of Computer Applications;2012, Vol. 60, p23
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
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.
ACCESSION #
89029749

 

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