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dc.contributor.author Shen, Qiang
dc.contributor.author Yang, Longzhi
dc.date.accessioned 2011-09-26T10:01:25Z
dc.date.available 2011-09-26T10:01:25Z
dc.date.issued 2011-09-26
dc.identifier.citation Shen , Q & Yang , L 2011 , ' Adaptive Fuzzy Interpolation with Prioritized Component Candidates ' pp. 428-435 . en
dc.identifier.other PURE: 180177
dc.identifier.other dspace: 2160/7587
dc.identifier.uri http://hdl.handle.net/2160/7587
dc.description L. Yang and Q. Shen, Adaptive fuzzy interpolation with prioritized component candidates, Proceedings of the 20th International Conference on Fuzzy Systems, pp.428-435, 2011. en
dc.description.abstract Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It first identifies all possible sets of faulty fuzzy reasoning components, termed the candidates, each of which may have led to all the contradictory interpolations. It then tries to modify one selected candidate in an effort to remove all the contradictions and thus restore interpolative consistency. This approach assumes that all the candidates are equally likely to be the real culprit. However, this may not be the case in real situations as certain identified reasoning components may be more liable to resulting in inconsistencies than others. This paper extends the adaptive approach by prioritizing all the generated candidates. This is achieved by exploiting the certainty degrees of fuzzy reasoning components and hence of derived propositions. From this, the candidate with the highest priority is modified first. This extension helps to quickly spot the real culprit and thus considerably improves the approach in terms of efficiency. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Adaptive Fuzzy Interpolation with Prioritized Component Candidates en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en


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