Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/54415
Title: Battery recharging prediction using extreme learning machine
Authors: Gao, Yan
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2013
Abstract: ELM (Extreme Learning Machine) is a newly developed algorithm working for SLFNs (single-hidden layer feedforward neural networks). It has better performance especially faster learning speed than other traditional learning methods, such as SVM (support vector machine). ELM can be used in a lot of applications with classification or regression requirements. Li-ion battery is a type of rechargeable battery which is widely used in daily life. It concerns the user when the battery will be out of charge. So it is necessary to inform the user to recharge the battery in advance. This report discusses how ELM can be applied in obtaining the time when the battery voltage drops below some certain voltage, i.e. 3V. Due to limited time, future work may be needed to make the prediction more applicable.
URI: http://hdl.handle.net/10356/54415
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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