Towards Backward Fuzzy Rule Interpolation

Shangzhu Jin, Ren Diao, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

1 Citation (SciVal)
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Abstract

Fuzzy rule interpolation (FRI) is well known forreducing the complexity of fuzzy models and making inferencepossible in sparse rule-based systems. However, in practicalfuzzy applications with inter-connected rule bases, situationsmay 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 todifferent 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 relateto the conclusion be inferred or interpolated from the knownantecedents and conclusion. B-FRI offers a way to broaden thefields of research and application of fuzzy rule interpolationand fuzzy inference. The steps of B-FRI implemented using thescale and move transformation-based fuzzy interpolation aregiven, along with two numerical examples to demonstrate thecorrectness and accuracy of the approach. Finally, a practicalexample is presented to show the applicability and potential of B-FRI.

Original languageEnglish
Title of host publicationProceedings of the 11th UK Workshop on Computational Intelligence
PublisherManchester University Press
Pages194-200
Publication statusPublished - 26 Sept 2011

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