In this paper, we use the information matrix technique to extract fuzzy if-then rules from data including noise. Witha normal diffusion function, we change all crisp observations of a given sample into fuzzy sets to make an information matrix. We extract rules according to the centroids of the rows of an information matrix. These rules are integrated into an additive fuzzy system with the same rule weight. Such fuzzy systems can be used as adaptive function approximators. Simulations show that this method is very effective compared with the conventional least-squares method and neural network. The best advantage of the suggested method is that, it may be the simplest way to extract fuzzy if-then rules from data.