Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/392
DC FieldValueLanguage
dc.contributor.authorAbdul Aziz N.H.en_US
dc.contributor.authorIbrahim Z.en_US
dc.contributor.authorAb Aziz N.A.en_US
dc.contributor.authorMuhammad B.en_US
dc.contributor.authorAb Rahman T.en_US
dc.contributor.authorMohamad, MSen_US
dc.contributor.authorRahmad S.A.en_US
dc.date.accessioned2021-01-17T06:33:05Z-
dc.date.available2021-01-17T06:33:05Z-
dc.date.issued2020-
dc.identifier.issn21954356-
dc.identifier.urihttp://hdl.handle.net/123456789/392-
dc.descriptionScopusen_US
dc.description.abstractSingle-solution simulated Kalman filter (ssSKF) is a variant of simulated Kalman filter (SKF) algorithm. Both algorithms employ the well-known Kalman filtering mechanism in an optimization process. Unlike the population-based SKF, the ssSKF operates using one agent. In this paper, parameter tuning of the ssSKF algorithm is presented.en_US
dc.language.isoenen_US
dc.publisherSpringer Nature Singapore Pte Ltd.en_US
dc.relation.ispartofLecture Notes in Mechanical Engineeringen_US
dc.subjectOptimizationen_US
dc.subjectSimulated Kalman filteren_US
dc.titleParameter tuning in the single-solution simulated Kalman filter optimizeren_US
dc.typeInternationalen_US
dc.relation.conference2nd Symposium on Intelligent Manufacturing and Mechatronics, SympoSIMM 2019en_US
dc.identifier.doi10.1007/978-981-13-9539-0_5-
dc.description.page48-56en_US
dc.date.seminarstartdate2019-07-08-
dc.date.seminarenddate2019-07-08-
dc.description.placeofseminarMelaka, Malaysiaen_US
dc.description.typeProceeding Papersen_US
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeInternational-
item.fulltextNo Fulltext-
crisitem.author.deptUniversiti Malaysia Kelantan-
Appears in Collections:Faculty of Bioengineering and Technology - Proceedings
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