Experience-based rule base generation and adaptation for fuzzy interpolation

Jie Li, Hubert Shum, Xin Fu, Graham Sexton, Longzhi Yang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Fuzzy modelling has been widely and successfully applied to control problems. Traditional fuzzy modelling requires either complete experts' knowledge or large data sets to generate rule bases such that the input spaces can be fully covered. Although fuzzy rule interpolation (FRI) relaxes this requirement by approximating rules using their neighbouring ones, it is still difficult for some real world applications to obtain sufficient experts' knowledge and/or data to generate a reasonable sparse rule base to support FRI. Also, the generated rule bases are usually fixed and therefore cannot support dynamic situations. In order to address these limitations, this paper presents a novel rule base generation and adaptation system to allow the creation of rule bases with minimal a priori knowledge. This is implemented by adding accurate interpolated rules into the rule base guided by a performance index from the feedback mechanism, also considering the rule's previous experience information as a weight factor in the process of rule selection for FRI. In particular, the selection of rules for interpolation in this work is based on a combined metric of the weight factors and the distances between the rules and a given observation, rather than being simply based on the distances. Two digitally simulated scenarios are employed to demonstrate the working of the proposed system, with promising results generated for both rule base generation and adaptation.
    Original languageEnglish
    Title of host publication2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
    Number of pages8
    ISBN (Electronic)9781509006267
    Publication statusPublished - 10 Nov 2016
    Event 2016 IEEE International Conference on Fuzzy Systems - Vancouver, Canada
    Duration: 24 Jul 201629 Jul 2016


    Conference 2016 IEEE International Conference on Fuzzy Systems
    Abbreviated titleFUZZ-IEEE


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