Publication:
Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection

Simple item page
dc.citedby11
dc.contributor.authorYap K.S.en_US
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55969610600en_US
dc.contributor.authorid15128307800en_US
dc.date.accessioned2023-12-29T07:43:41Z
dc.date.available2023-12-29T07:43:41Z
dc.date.issued2013
dc.description.abstractThe fuzzy rule sets, which have been derived from the hybrid neural network model, called the O-EGART-PR-FIS, is an integration of the Adaptive Resonance Theory (ART) into Generalized Regression Neural Network (GRNN), display substantial redundancy and low interpretability that leads to time-consuming prediction process. The O-EGART-PR-FIS approach can achieve the highest accuracy rate among all, however the extracted rules are less compact. Hence, in this paper, we propose a genetic algorithm based method with the inclusion of the 'Don't Care' antecedent (hereafter denoted as DC-GA) to the foundation of the O-EGART-PR-FIS, with the aim of further optimizing the existing fuzzy rules. The improved model is applied to two benchmark problems, and the rules extracted are analyzed, discussed and compared with other published methods. From the comparison results, it is observed that the improved model is attested to be statistically superior to other ANN models. Therefore, it reveals the efficacy of DC-GA in eliciting a set of compact and yet easily comprehensible rules while sustaining a high classification performance. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6648106
dc.identifier.doi10.1109/ETFA.2013.6648106
dc.identifier.scopus2-s2.0-84890723619
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84890723619&doi=10.1109%2fETFA.2013.6648106&partnerID=40&md5=8264dab83d42c6a9b9012d5adbd28de1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29942
dc.sourceScopus
dc.sourcetitleIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
dc.subjectFault Detection
dc.subjectFuzzy Inference System
dc.subjectGenetic Algorithm
dc.subjectRule Extraction
dc.subjectFactory automation
dc.subjectFault detection
dc.subjectGenetic algorithms
dc.subjectNeural networks
dc.subjectAdaptive resonance theory
dc.subjectBench-mark problems
dc.subjectClassification performance
dc.subjectFuzzy inference systems
dc.subjectGeneralized Regression Neural Network(GRNN)
dc.subjectHybrid neural networks
dc.subjectPrediction process
dc.subjectRule extraction
dc.subjectFuzzy rules
dc.titleCompressing and improving fuzzy rules using genetic algorithm and its application to fault detectionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS