Fuzzy Logic and Genetic Algorithm for Optimizing the Approximate Match of Rules based on Backpropagation Neural Networks.
Jun Srisutapan, Boonserm Kijsirikul · Fuzzy Systems and Knowledge Discovery · 2002
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.