Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150569
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dc.contributor.authorFerdaus, Md Meftahulen_US
dc.contributor.authorPratama, Mahardhikaen_US
dc.contributor.authorAnavatti, Sreenatha G.en_US
dc.contributor.authorGarratt, Matthew A.en_US
dc.date.accessioned2021-06-07T03:30:06Z-
dc.date.available2021-06-07T03:30:06Z-
dc.date.issued2019-
dc.identifier.citationFerdaus, M. M., Pratama, M., Anavatti, S. G. & Garratt, M. A. (2019). Online identification of a rotary wing Unmanned Aerial Vehicle from data streams. Applied Soft Computing, 76, 313-325. https://dx.doi.org/10.1016/j.asoc.2018.12.013en_US
dc.identifier.issn1568-4946en_US
dc.identifier.other0000-0002-8833-2274-
dc.identifier.other0000-0001-6531-5087-
dc.identifier.other0000-0003-0222-430X-
dc.identifier.urihttps://hdl.handle.net/10356/150569-
dc.description.abstractUntil now the majority of the neuro and fuzzy modeling and control approaches for rotary wing Unmanned Aerial Vehicles (UAVs), such as the quadrotor, have been based on batch learning techniques, therefore static in structure, and cannot adapt to rapidly changing environments. Implication of Evolving Intelligent System (EIS) based model-free data-driven techniques in fuzzy system are good alternatives, since they are able to evolve both their structure and parameters to cope with sudden changes in behavior, and performs perfectly in a single pass learning mode which is suitable for online real-time deployment. The Metacognitive Scaffolding Learning Machine (McSLM) is seen as a generalized version of EIS since the metacognitive concept enables the what-to-learn, how-to-learn, and when-to-learn scheme, and the scaffolding theory realizes a plug-and-play property which strengthens the online working principle of EISs. This paper proposes a novel online identification scheme, applied to a quadrotor using real-time experimental flight data streams based on McSLM, namely Metacognitive Scaffolding Interval Type 2 Recurrent Fuzzy Neural Network (McSIT2RFNN). Our proposed approach demonstrated significant improvements in both accuracy and complexity against some renowned existing variants of the McSLMs and EISs.en_US
dc.language.isoenen_US
dc.relation.ispartofApplied Soft Computingen_US
dc.rights© 2018 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleOnline identification of a rotary wing Unmanned Aerial Vehicle from data streamsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.asoc.2018.12.013-
dc.identifier.scopus2-s2.0-85059117819-
dc.identifier.volume76en_US
dc.identifier.spage313en_US
dc.identifier.epage325en_US
dc.subject.keywordsEvolvingen_US
dc.subject.keywordsFuzzyen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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