Generation of Fuzzy Rules Based on Complex-valued Neuro-Fuzzy Learning Algorithm

Abstract

In order to generate or tune fuzzy rules, Neuro-Fuzzy learning algorithms with Gaussian type membership functions based on gradient-descent method are well known. In this paper, we propose a new learning approach, the Complex-valued Neuro-Fuzzy learning algorithm. This method is an extension of the conventional method to complex domain by using a complex-valued neural network that maps complex values to real values. Input, antecedent membership functions and consequent singleton are complex, and output is real. Two-dimensional input can be better represented by complex numbers than by real values. We compared it with the conventional method by several function identification problems, and revealed that the proposed method outperformed the counterpart, and that it is a useful tool for learning a fuzzy system model.The 3rd International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII 2013) will be held in Shanghai, China from October 18 to 21 in 2013