A new framework with similarity reasoning and monotone fuzzy rule relabeling for fuzzy inference systems
Contribuinte(s) |
[Unknown] |
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Data(s) |
01/01/2013
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30057152/evid-conffuzzieee-rvwgnl-2013.pdf http://dro.deakin.edu.au/eserv/DU:30057152/tay-newframewordwith-2013.pdf |
Direitos |
2013, IEEE |
Palavras-Chave | #fuzzy inference system #monotonicity property #fuzzy rule relabeling #application frameworks #failure mode and effect analysis |
Tipo |
Conference Paper |