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A new framework with similarity reasoning and monotone fuzzy rule relabeling for fuzzy inference systems

conference contribution
posted on 2013-01-01, 00:00 authored by K Tay, L Pang, T Jee, Chee Peng LimChee Peng Lim
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.



Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)


1 - 8




Hyderabad, India

Place of publication

Piscataway, N.J.

Start date


End date




Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems