A New Framework With Similarity Reasoning and Monotone Fuzzy Rule Relabeling for Fuzzy Inference Systems
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 t...
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| Main Authors: | , , |
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| Format: | E-Article |
| Language: | English |
| Published: |
IEEE
2013
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| Subjects: | |
| 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/ |
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| Summary: | 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. |
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