dc.contributor.author Greenfield, Sarah en dc.contributor.author Chiclana, Francisco dc.date.accessioned 2013-06-06T13:51:13Z dc.date.available 2013-06-06T13:51:13Z dc.date.issued 2013 dc.identifier.citation Greenfield, S. and Chiclana, F. (2013) Defuzzification of the Discretised Generalised Type-2 Fuzzy Set: Experimental Evaluation. Information Sciences, 244, pp. 1-25 en dc.identifier.uri http://hdl.handle.net/2086/8710 dc.description CCI - Centre for Computational Intelligence NOTICE: this is the author’s version of a work that was accepted for publication in Information Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version can be found by following the DOI en dc.description.abstract The work reported in this paper addresses the challenge of the efficient and accurate defuzzification of discretised generalised type-2 fuzzy sets as created by the inference stage of a Mamdani Fuzzy Inferencing System. The exhaustive method of defuzzification for type-2 fuzzy sets is extremely slow, owing to its enormous computational complexity. Several approximate methods have been devised in response to this defuzzification bottleneck. In this paper we begin by surveying the main alternative strategies for defuzzifying a generalised type-2 fuzzy set: (1) Vertical Slice Centroid Type-Reduction; (2) the sampling method; (3) the elite sampling method; and (4) the $\alpha$-planes method. en We then evaluate the different methods experimentally for accuracy and efficiency. For accuracy the exhaustive method is used as the standard. The test results are analysed statistically by means of the Wilcoxon Nonparametric Test and the elite sampling method shown to be the most accurate. In regards to efficiency, Vertical Slice Centroid Type-Reduction is demonstrated to be the fastest technique. dc.language.iso en en dc.publisher Elsevier en dc.subject Type-2 fuzzy sets en dc.subject Defuzzification en dc.subject Sampling Method en dc.subject $\alpha$-Planes Method en dc.subject VSCTR en dc.title Defuzzification of the Discretised Generalised Type-2 Fuzzy Set: Experimental Evaluation en dc.type Article en dc.identifier.doi http://dx.doi.org/10.1016/j.ins.2013.04.032 dc.researchgroup DIGITS en dc.peerreviewed Yes en dc.researchinstitute Institute of Artificial Intelligence (IAI) en
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