An improved method of fuzzy approximate reasoning by combining self-organizing feature map and fuzzy logic

This paper proposes a new type of fuzzy approximate reasoning method that combines a self organizing feature map and fuzzy logic. Previous methods considered only the input to determine the number of fuzzy rules, while this paper considers both input and output simultaneously. Our approach proved to improve the inference performance. We also developed a new index for avoiding overlearning which guarantees more accurate results. Experimental results showed that our approach surpasses the performance of Takagi & Hayashi approach.