Automated Extraction of Fuzzy IF-THEN Rules Using Neural Networks
Yoichi Hayashi, Masato Nakai · IEEJ Transactions on Electronics Information and Systems · 1990
Neural network models and the automatic generation of expert systems based on learning processes are attracting growing interest as useful tools for mainstream tasks involving artificial intelligence. Neural networks embody the information derived from the training data (examples) and are implicity assumed to contain the IF-THEN rules and/or knowledge base used for expert systems.This paper proposes a method to extract automatically fuzzy IF-THEN rules with “linguistic relative importance” of each proposition in an antecedent by using a feed-forward neural network. The linguistic relative importance which is defined by a fuzzy set represents the degree of effect of each proposition on consequence. By providing linguistic relative importance for each proposition, each fuzzy IF-THEN rule has more flexible expression than that of ordinary IF-THEN rules. Furthermore, truthfulness of each fuzzy IF-THEN rule is given in the form of linguistic truth value which is defined by a fuzzy set. Enhancement of knowledge presentation capability and flexibility by using the fuzzy IF-THEN rules with linguistic relative importance facilitates the automated extraction of IF-THEN rules from neural networks. First, we give an algorithm to select propositions in an antecedent (IF-part), that is, to extract framework of fuzzy IF-THEN rules. Second, we show a method to give truthfulness of the extracted fuzzy IF-THEN rules. Furthermore, we propose a method to determine linguistic relative importance of each proposition in an antecedent. In order to prove the validity of the proposed method, an illustrative example is solved.