Extreme learning machine based robotic arm modeling

dc.contributor.author	Alcin, O.F. and Ucar, F. and Korkmaz, D.
dc.date.accessioned	2021-04-08T12:08:29Z
dc.date.available	2021-04-08T12:08:29Z
dc.date.issued	2016
dc.identifier	10.1109/MMAR.2016.7575302
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991740123&doi=10.1109%2fMMAR.2016.7575302&partnerID=40&md5=ec85b550c72392de75186731163eace6
dc.identifier.uri	http://acikerisim.bingol.edu.tr/handle/20.500.12898/4618
dc.description.abstract	Robotic arms are very powerful machines that can be used in many various applications in industry. So that, a suitable dynamic model is derived to verify that performs the tasks. But, dynamic equation is an important issue due to its complexity. Thus, an alternative model can be derived for the robotic arms. This paper is proposed Extreme Learning Machine (ELM) model for the angular acceleration of a robotic arm. The performance of the ELM model is performed by using Pumadyn datasets. At the same time, the validation of the proposed model is compared with Artificial Neural Network (ANN). Experimental results show that the proposed model is suitable and it provides low computation complexity. © 2016 IEEE.
dc.language.iso	English
dc.source	2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016
dc.title	Extreme learning machine based robotic arm modeling

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