Adaptive fuzzy interpolation with prioritized component candidates

Longzhi Yang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

15 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2011 IEEE International Conference on Fuzzy Systems (FUZZ)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages428-435
ISBN (Print)9781424473151
DOIs
Publication statusPublished - 2011
EventFuzzy Systems (FUZZ), 2011 IEEE International Conference on - Taipei
Duration: 1 Jan 2011 → …

Conference

ConferenceFuzzy Systems (FUZZ), 2011 IEEE International Conference on
Period1/01/11 → …

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