Adaptive fuzzy interpolation with prioritized component candidates

Yang, Longzhi and Shen, Qiang (2011) Adaptive fuzzy interpolation with prioritized component candidates. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ), 27-30 June 2011, Taipei.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: assumption-based truth maintenance systems, reliability-based general diagnostic engine
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 29 Jun 2016 07:56
Last Modified: 12 Oct 2019 22:52

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