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dc.contributor.author Diao, Ren
dc.contributor.author Shen, Qiang
dc.contributor.author Jin, Shangzhu
dc.date.accessioned 2011-09-26T10:05:32Z
dc.date.available 2011-09-26T10:05:32Z
dc.date.issued 2011-09-26
dc.identifier.citation Diao , R , Shen , Q & Jin , S 2011 , ' Towards Backward Fuzzy Rule Interpolation ' . en
dc.identifier.other PURE: 180002
dc.identifier.other dspace: 2160/7591
dc.identifier.uri http://hdl.handle.net/2160/7591
dc.description S. Jin, R. Diao, and Q. Shen, Towards Backward Fuzzy Rule Interpolation, Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI2011), 2011. en
dc.description.abstract Fuzzy rule interpolation (FRI) is well known for reducing the complexity of fuzzy models and making inference possible in sparse rule-based systems. However, in practical fuzzy applications with inter-connected rule bases, situations may arise when a crucial antecedent of observation is absent, either due to human error or difficulty in obtaining data, while the associated conclusion may be derived according to different rules or even observed directly. To address such issues, a concept termed Backward Fuzzy Rule Interpolation (B-FRI) is proposed, allowing the observations which directly relate to the conclusion be inferred or interpolated from the known antecedents and conclusion. B-FRI offers a way to broaden the fields of research and application of fuzzy rule interpolation and fuzzy inference. The steps of B-FRI implemented using the scale and move transformation-based fuzzy interpolation are given, along with two numerical examples to demonstrate the correctness and accuracy of the approach. Finally, a practical example is presented to show the applicability and potential of B-FRI. en
dc.language.iso eng
dc.title Towards Backward Fuzzy Rule Interpolation en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en


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