Microcontroller Compatible Sealed Lead Acid Battery Remaining Energy Prediction Using Adaptive Neural Fuzzy Inference System

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dc.contributor.authorAkdemir, Bayram
dc.contributor.authorGunes, Salih
dc.contributor.authorComlekciler, Ismail Taha
dc.date.accessioned2020-03-26T17:39:15Z
dc.date.available2020-03-26T17:39:15Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Conference on Web Information Systems and Mining -- NOV 07-08, 2009 -- Shanghai, PEOPLES R CHINAen_US
dc.description.abstractAll over the world, many portable devices need battery to run. Every expert has to use efficient hardware and software documentation to make battery last longer and make a correlation between microcontrollers' duties and the remaining energy of batteries. In order to make battery last longer, battery information must be evaluated continuously. In many devices, fluctuating current is used due to its own load so alternating current makes it hard to compute the remaining battery level. For many devices, there could be battery level indicator as solution. This solution gives clue about the remaining time for user but it does not give any hint for microcontroller about battery situation. For low cost devices, it could be very difficult to estimate the remaining storage energy in the battery. In this study, microcontroller compatible sealed lead acid battery remaining energy predictor based on adaptive neural fuzzy inference system has been designed and proposed. In order to test proposed method, mean absolute error and leave one out have been used to measure proposed system performance. The obtained mean absolute error results for leave one out is 10.55, epoch error is 11.72. Through the study, low adaptive neural fuzzy inference system rules and low microcontroller memory consumption were aimed.en_US
dc.description.sponsorshipIEEE, Springer, APNNA, NSFCen_US
dc.identifier.doi10.1109/WISM.2009.161en_US
dc.identifier.endpage783en_US
dc.identifier.isbn978-0-7695-3817-4
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage779en_US
dc.identifier.urihttps://dx.doi.org/10.1109/WISM.2009.161
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23679
dc.identifier.wosWOS:000275860800153en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEE COMPUTER SOCen_US
dc.relation.ispartofWISM: 2009 INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND MINING, PROCEEDINGSen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural fuzzy inference systemen_US
dc.subjectSLA batteriesen_US
dc.subjectmicrocontrolleren_US
dc.subjectpredictionen_US
dc.titleMicrocontroller Compatible Sealed Lead Acid Battery Remaining Energy Prediction Using Adaptive Neural Fuzzy Inference Systemen_US
dc.typeConference Objecten_US

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