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A novel 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 a fuzzy logic control based on weighted rules depending on output performances using a neural network and we derive a weight updating algorithm. To guarantee convergence of the weights, a learning rate is developed by introducing a Lyapunov function. With the final weight change information, we can make better decisions by taking into consideration conflicting rules. The proposed method is applied to simple problems and simulation results are included. And real applications of the proposed method are also discussed.