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dc.contributor.author Yang, Longzhi
dc.contributor.author Shen, Qiang
dc.date.accessioned 2009-09-10T10:02:27Z
dc.date.available 2009-09-10T10:02:27Z
dc.date.issued 2009
dc.identifier.citation Yang , L & Shen , Q 2009 , ' Extending Adaptive Interpolation: From Triangular to Trapezoidal ' pp. 25-30 . en
dc.identifier.other PURE: 124285
dc.identifier.other dspace: 2160/3026
dc.identifier.uri http://hdl.handle.net/2160/3026
dc.identifier.uri http://ima.ac.uk/ukci2009/index.shtml en
dc.description L. Yang and Q. Shen. Extending adaptive interpolative reasoning: from triangular to trapezoidal. Proceedings of the 2009 UK Workshop on Computational Intelligence, pp. 25-30, 2009. en
dc.description.abstract Fuzzy interpolative reasoning strengthens the power of fuzzy inference by enhancing the robustness of fuzzy systems and reducing systems complexity. However, during the interpolation process, it is possible that multiple object values for a common variable are inferred which may lead to inconsistency in interpolated results. A novel approach [10] was recently proposed for identification and correction of defective rules in the transformations computed for interpolation, thereby removing the inconsistencies. However, the implementation of this work is limited to rule models involving triangular fuzzy variables. This paper extends the adaptive approach as presented in [10], by introducing trapezoidal variables in the representation and manipulation of fuzzy rule models. This significantly improves the applicability of adaptive fuzzy interpolation reasoning, as many fuzzy systems are modelled with trapezoidal (as well as triangular) variables. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Extending Adaptive Interpolation: From Triangular to Trapezoidal en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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