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Optimization of gaussian fuzzy membership functions and evaluation of the monotonicity property of fuzzy inference systems
In this paper, two issues relating to modeling of a monotonicity-preserving Fuzzy Inference System (FIS) are examined. The first is on designing or tuning of Gaussian Membership Functions (MFs) for a monotonic FIS. Designing Gaussian MFs for an FIS is difficult because of its spreading and curvature characteristics. In this study, the sufficient conditions are exploited, and the procedure of designing Gaussian MFs is formulated as a constrained optimization problem. The second issue is on the testing procedure for a monotonic FIS. As such, a testing procedure for a monotonic FIS model is proposed. Applicability of the proposed approach is demonstrated with a real world industrial application, i.e., Failure Mode and Effect Analysis. The results obtained are analysis and discussed. The outcomes show that the proposed approach is useful in designing a monotonicity-preserving FIS model.
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Event
International Conference on Fuzzy Systems (2011 : Taipei, Taiwan)Pagination
1219 - 1224Publisher
IEEE Computer SocietyLocation
Taipei, TaiwanPlace of publication
Los Alamitos, Calif.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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