Planar Robot Arm Performance: Analysis with Feedforward Neural Networks
Abraham Antonio LÃ³pez Villarreal, Samuel GonzÃ¡lez-LÃ³pez, Luis Arturo Medina MuÃ±oz
Journal of Networking Technology, Vol. 8, No. 2, 2017.
DOI: 
Link: http://www.dline.info/jnt/fulltext/v8n2/jntv8n2_2.pdf
Abstract:
The purpose of this paper is to define the best training algorithm and activation function during the training process of a two degree of freedom planar robot arm that can be used to approach SCARA applications. A feed-forward neural network with two hidden layers was trained with several training algorithms such as Levenberg-Marquardt, Bayesian Regularization and others, and the activation functions such as symmetric sigmoid, logarithmic sigmoid and linear transfer to compare the resulting error and look for optimal performance.