Abstract
In this paper, we discuss a fuzzy classifier with pyramidal membership functions and discuss a performance improvement by changing linear membership functions to quadratic membership functions. First, we define the classifier with quadratic membership functions and then discuss the training method that maximizes the recognition rate by counting the net increase in the recognition rate by changing the slope and the center of each membership function. Finally, we demonstrate the recognition improvement by quadratic membership functions for the iris and blood cell data sets.