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An evolutionary-based similarity reasoning scheme for monotonic multi-input fuzzy inference systems
In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.
History
Event
International Conference on Fuzzy Systems (2011 : Taipei, Taiwan)Pagination
442 - 447Publisher
IEEE Computer SocietyLocation
Taipei, TaiwanPlace of publication
Los Alamitos, Calif.Publisher DOI
Start date
2011-06-27End date
2011-06-30ISSN
1098-7584ISBN-13
9781424473151ISBN-10
1424473152Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2011, IEEETitle of proceedings
FUZZ 2011 : Proceedings of the IEEE International Conference on Fuzzy SystemsUsage metrics
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