Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/400
DC FieldValueLanguage
dc.contributor.authorAziz N.A.A.en_US
dc.contributor.authorAziz N.H.A.,en_US
dc.contributor.authorMuhammad B.,en_US
dc.contributor.authorIbrahim Z.,en_US
dc.contributor.authorMubin M.,en_US
dc.contributor.authorMokhtar N.,en_US
dc.contributor.authorMohamad, MSen_US
dc.date.accessioned2021-01-17T06:44:14Z-
dc.date.available2021-01-17T06:44:14Z-
dc.date.issued2020-
dc.identifier.issn18761100-
dc.identifier.urihttp://hdl.handle.net/123456789/400-
dc.descriptionScopusen_US
dc.description.abstractThe original Simulated Kalman Filter (SKF) is an optimizer that employs synchronous update mechanism. The agents in SKF update their solutions after all fitness calculations, prediction process, and measurement process are completed. An alternative to synchronous update is asynchronous update. In asynchronous update, only one agent does fitness calculation, prediction, measurement, and estimation processes at one time. Recent study found that the original SKF is subjected to premature convergence. Thus, synchronous and asynchronous mechanisms are combined in SKF to address the premature convergence problem in SKF. At first, the SKF starts with synchronous update. If no improved solution is found, the SKF changes its update mechanism. The decision to switch from synchronous to asynchronous or vice versa is made based on the information of the population. In this paper, population’s diversity is used as switching indicator. Using the CEC2014 benchmark test suite, experimental results indicate that the proposed diversity-based adaptive switching synchronous-asynchronous SKF outperforms the original SKF significantly.en_US
dc.language.isoenen_US
dc.publisherSpringer Nature Singapore Pte Ltd.en_US
dc.relation.ispartofLecture Notes in Electrical Engineeringen_US
dc.subjectAsynchronousen_US
dc.subjectSimulated kalman filteren_US
dc.subjectSynchronousen_US
dc.titleA Diversity-Based Adaptive Synchronous-Asynchronous Switching Simulated Kalman Filter Optimizeren_US
dc.typeInternationalen_US
dc.relation.conferenceInternational Conference on Electrical, Control and Computer Engineeringen_US
dc.identifier.doi10.1007/978-981-15-2317-5_11-
dc.description.page113-126en_US
dc.volume632en_US
dc.date.seminarstartdate2019-07-29-
dc.date.seminarenddate2019-07-29-
dc.description.placeofseminarKuantan, Malaysiaen_US
dc.description.typeProceeding Papersen_US
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeInternational-
crisitem.author.deptUniversiti Malaysia Kelantan-
Appears in Collections:Faculty of Bioengineering and Technology - Proceedings
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