Fuzzy Logic and Genetic Algorithm for Optimizing the Approximate Match of Rules based on Backpropagation Neural Networks
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چکیده
This paper presents an application of Fuzzy Logic(FL) and Genetic Algorithm(GA) for improving the approximate match of first-order Inductive Logic Programming(ILP) rules that is based on Backpropagation Neural Networks(BNN). With the help of FL, the evaluation of the truth values of logic programs is more problem-sophisticated, before these values are sent to the BNN for learning or for recognising. We employ GA to find the best fuzzy sets. Experimental results on a ThaiOCR domain show that the our method gives the best recognition accuracy of 85.95% compared to 82.31% recognition accuracy of the previous method.
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تاریخ انتشار 2002