Adjusting Membership Functions in Fuzzy Rule-Based Classification Systems
Hisao ISHIBUCHI, Takehiko MORISAWA
This paper proposes a learning method for adjusting the membership functions of antecedent fuzzy sets of each fuzzy rule in order to construct a fuzzy rule-based classification system. In the proposed method, three parameters that specify a non-symmetric triangular membership function are adjusted by an error-correction-based learning scheme. The grade of certainty of each fuzzy rule is also adjusted together with the membership functions of its antecedent fuzzy sets. The proposed method is illustrated by applying it to a simple two-dimensional pattern classification problem. Moreover, the effectiveness of the proposed method is demonstrated by computer simulations on some multi-dimensional pattern classification problems. Finally, an additional learning method for the fine tuning of classification boundaries between different classes is discussed.