A New Framework With Similarity Reasoning and Monotone Fuzzy Rule Relabeling for Fuzzy Inference Systems

• Main Authors: Tay, Kai Meng, Liew, Meng Pang, Tze, Ling Jee
• Format: E-Article
• Language: English
• Published: IEEE 2013
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://ir.unimas.my/id/eprint/16639/1/A%20New%20Framework%20With%20Similarity%20Reasoning%20%28abstract%29.pdf; http://ir.unimas.my/id/eprint/16639/; http://ieeexplore.ieee.org/document/6622455/

A complete and monotonically-ordered fuzzy rule base is necessary to maintain the monotonicity property of a Fuzzy Inference System (FIS). In this paper, a new monotone fuzzy rule relabeling technique to relabel a non-monotone fuzzy rule base provided by domain experts is proposed. Even though the Genetic Algorithm (GA)-based monotone fuzzy rule relabeling technique has been investigated in our previous work [7], the optimality of the approach could not be guaranteed. The new fuzzy rule relabeling technique adopts a simple brute force search, and it can produce an optimal result. We also formulate a new two-stage framework that encompasses a GA-based rule selection scheme, the optimization based-Similarity Reasoning (SR) scheme, and the proposed monotone fuzzy rule relabeling technique for preserving the monotonicity property of the FIS model. Applicability of the two-stage framework to a real world problem, i.e., failure mode and effect analysis, is further demonstrated. The results clearly demonstrate the usefulness of the proposed framework.