Knowledge acquisition from input-output data by fuzzy-neural systems

We attempt to model the operation of a nonlinear system with a set of input-output data. First, we use the method of fuzzy partitions to cluster the data into several groups and give each group a fuzzy rule to describe the distribution of associated data. A rough fuzzy rule based model can be constructed by combining these generated rules. Next, for the purpose of higher precision, we use a fuzzy-neural network to improve the rules obtained previously by tuning the shapes of membership functions. We adopt the trapezoidal membership functions instead of Gaussian ones as proposed by Lin et al. (1997), and show that the trapezoidal model is better than the Gaussian one. Finally, we can easily extract the improved rules from the network to give more precise inference of the fuzzy rule based model. There are two advantages of this method: 1) the fuzzy-neural network can be trained rapidly; and 2) one can extract symbolic rules from the numerical weights of the fuzzy-neural network. Such a method is very simple and the simulated results are satisfactory