One method of translating the fuzzy rules into neural network of the fault diagnosis system

Based on the analysis of fuzzy rules and the certainty factor transitive algorithm of the fault diagnosis system, one method of translating the fuzzy rules into an artificial neural network is researched. According to the fuzzy rules and its certainty factor transitive algorithm, the teacher samples of the neural network can be obtained. The weights and thresholds matrix of the neural network can be obtained by samples learning, so the new fault samples can be diagnosed. According to the diagnosis result obtained from the testing samples, this method can diagnose rapidly, it also has strong generalization ability and high accuracy, at the same time the method can eliminate the conflict in the reasoning process