Experience-based Rule Base Generation and Adaptation for Fuzzy Interpolation

Jie Li, Hubert P. H. Shum, Xin Fu, Graham Sexton, Longzhi Yang

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Abstract

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
Subtitle of host publication(FUZZ-IEEE)
PublisherIEEE
Pages102-109
Number of pages8
Volume2016
ISBN (Electronic)9781509006267
DOIs
Publication statusPublished - 10 Nov 2016
Event2016 IEEE International Conference on Fuzzy Systems - Vancouver Convention Centre, Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7737659

Publication series

Name2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
PublisherIEEE
ISSN (Print)1544-5615

Conference

Conference2016 IEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE
CountryCanada
CityVancouver
Period24/07/1629/07/16
Internet address

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