Characterization of Hyperparameter of Learning Type Trapezoidal Fuzzy Inference

Abstract

Learning type trapezoidal fuzzy inference is a general form of triangular fuzzy inference, and fuzzy clustering using fuzzy inference can identify the class boundary area by learning. It is necessary to adjust the trapezoidal membership function of the antecedent part and the singleton real value of the consequent part with the learning mechanism. In addition, initial value setting of parameters is an important factor for learning for minimizing error. In particular, the initial value of the singleton in the consequent part greatly changes the accuracy of the estimation of fuzzy inference depending on the set value. In this paper, we will discuss the characteristics of the hyperparameters to determine fuzzy rules of class discrimination with minimizing error. In addition, we discuss the influence of the learning parameters of the antecedent part and the consequent part on error accuracy, and the procedure of learning for improving learning. By the numerical examples, several combinations of extensive hyperparameters are discussed by significance test in order to acquire the optimal fuzzy rule of fuzzy clustering.