Fuzzy logic control based on weighted rules using neural network

In this paper a method of fuzzy logic control based on possibly inconsistent if-then rules representing uncertain knowledge or imprecise data is studied. When it is hard to obtain consistent rule bases, we propose the fuzzy logic control based on weighted rules depending on output performances using the neural network and we derive the weight updating algorithm. With the final weight change informations, we can make better decisions by taking into consideration conflicting rules. Computer simulations show the proposed method has good performance to deal with the inconsistent rule base in the fuzzy logic control. And real application problems are also discussed.