Adaptive Fuzzy Interpolation

Yang, Longzhi and Shen, Qiang (2011) Adaptive Fuzzy Interpolation. IEEE Transactions on Fuzzy Systems, 19 (6). pp. 1107-1126. ISSN 1063-6706

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Fuzzy interpolative reasoning strengthens the power of fuzzy inference by the enhancement of the robustness of fuzzy systems and the reduction of the systems' complexity. However, after a series of interpolations, it is possible that multiple object values for a common variable are inferred, leading to inconsistency in interpolated results. Such inconsistencies may result from defective interpolated rules or incorrect interpolative transformations. This paper presents a novel approach for identification and correction of defective rules in interpolative transformations, thereby removing the inconsistencies. In particular, an assumption-based truth-maintenance system (ATMS) is used to record dependences between interpolations, and the underlying technique that the classical general diagnostic engine (GDE) employs for fault localization is adapted to isolate possible faulty interpolated rules and their associated interpolative transformations. From this, an algorithm is introduced to allow for the modification of the original linear interpolation to become first-order piecewise linear. The approach is applied to a realistic problem, which predicates the diarrheal disease rates in remote villages, to demonstrate the potential of this study.

Item Type: Article
Uncontrolled Keywords: Assumption-based truth maintenance, fuzzy rule interpolation, general diagnostic engine
Subjects: G400 Computer Science
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ay Okpokam
Date Deposited: 25 Jun 2013 11:08
Last Modified: 13 Oct 2019 00:31

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