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dc.contributor.authorRattadilok, Prapa
dc.contributor.authorPetrovski, Andrei
dc.date.accessioned2015-02-12T10:36:07Z
dc.date.available2015-02-12T10:36:07Z
dc.date.issued2014-12
dc.identifier.citationRATTADILOK, P. and PETROVSKI, A., 2014. Self-learning data processing framework based on computational intelligence: enhancing autonomous control by machine intelligence. In: Proceedings of IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 2014. 9-12 December 2014. Piscataway, NJ: IEEE. pp. 87-94.en
dc.identifier.isbn9781479944941en
dc.identifier.urihttp://hdl.handle.net/10059/1144
dc.description.abstractA generic framework for evolving and autonomously controlled systems has been developed and evaluated in this paper. A three-phase approach aimed at identification, classification of anomalous data and at prediction of its consequences is applied to processing sensory inputs from multiple data sources. An ad-hoc activation of sensors and processing of data minimises the quantity of data that needs to be analysed at any one time. Adaptability and autonomy are achieved through the combined use of statistical analysis, computational intelligence and clustering techniques. A genetic algorithm is used to optimise the choice of data sources, the type and characteristics of the analysis undertaken. The experimental results have demonstrated that the framework is generally applicable to various problem domains and reasonable performance is achieved in terms of computational intelligence accuracy rate. Online learning can also be used to dynamically adapt the system in near real time.en
dc.language.isoenen
dc.publisherIEEEen
dc.rights“© © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” RATTADILOK, P. and PETROVSKI, A., 2014. Self-learning data processing framework based on computational intelligence: enhancing autonomous control by machine intelligence. In: Proceedings of IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 2014. 9-12 December 2014. Piscataway, NJ: IEEE. pp. 87-94.DOI - http://dx.doi.org/10.1109/EALS.2014.7009508en
dc.subjectComputational intelligenceen
dc.subjectEvolving and autonomous systemsen
dc.subjectAnomaliesen
dc.subjectRobot controlen
dc.titleSelf-learning data processing framework based on computational intelligence: enhancing autonomous control by machine intelligence.en
dc.typeConference publicationsen
dc.publisher.urihttp://dx.doi.org/10.1109/EALS.2014.7009508en


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