Automatic Programming Methodologies for Electronic Hardware Fault Monitoring

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dc.creatorAbraham,Ajith
dc.creatorGrosan,Crina
dc.date2006
dc.date.accessioned2024-02-06T12:54:20Z
dc.date.available2024-02-06T12:54:20Z
dc.descriptionThis paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the stressor — susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.
dc.formattext/html
dc.identifierhttps://doi.org/10.3217/jucs-012-04-0408
dc.identifierhttps://lib.jucs.org/article/28602/
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/8998
dc.languageen
dc.publisherJournal of Universal Computer Science
dc.relationinfo:eu-repo/semantics/altIdentifier/eissn/0948-6968
dc.relationinfo:eu-repo/semantics/altIdentifier/pissn/0948-695X
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsJ.UCS License
dc.sourceJUCS - Journal of Universal Computer Science 12(4): 408-431
dc.subjectgenetic programming
dc.subjectneural networks
dc.subjectdecision trees
dc.subjectfault monitoring
dc.subjectcomputational intelligence
dc.subjectelectronic hardware
dc.titleAutomatic Programming Methodologies for Electronic Hardware Fault Monitoring
dc.typeResearch Article
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