Monotone fuzzy rule relabeling for the zero-order TSK fuzzy inference system

To maintain the monotonicity property of a fuzzy inference system, a monotonically ordered and complete set of fuzzy rules is necessary. However, monotonically ordered fuzzy rules are not always available, e.g., errors in human judgments lead to nonmonotone fuzzy rules. The focus of this paper is on a new monotone fuzzy rule relabeling (MFRR) method that is able to relabel a set of nonmonotone fuzzy rules to meet the monotonicity property with reduced computation. Unlike the brute-force approach, which is susceptible to the combinatorial explosion problem, the proposed MFRR method explores within a reduced search space to find the solutions, therefore decreasing the computational requirements. The usefulness of the proposed method in undertaking failure mode and effect analysis problems is demonstrated using publicly available information. The results indicate that the MFRR method can produce optimal solutions with reduced computational time.