二次のメンバーシップ関数による超直方体領域を持つファジィクラシファイアの汎化能力の向上

抄録

In this paper we discuss a fuzzy classifier with hyperbox regions which has 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 when the slope and the center of a membership function are separately changed. Finally, we demonstrate the recognition improvement by quadratic membership functions for four data sets.