Publications:Intelligent deflection yoke magnetic field tuning


Do not edit this section

Keep all hand-made modifications below

Title Intelligent deflection yoke magnetic field tuning
Author Antanas Verikas and Marija Bacauskiene and A. Dosinas and V. Bartkevicius and Adas Gelzinis and M. Vaitkunas and Arunas Lipnickas
Year 2004
PublicationType Journal Paper
Journal Journal of Intelligent Manufacturing
Diva url
Abstract This paper presents a method and a system to identify the number of magnetic correction shunts and their positions for deflection yoke tuning to correct the misconvergence of colors of a cathode ray tube. The method proposed consists of two phases, namely, learning and optimization. In the learning phase, the radial basis function neural network is trained to learn a mapping: correction shunt position --> changes in misconvergence. In the optimization phase, the trained neural network is used to predict changes in misconvergence depending on a correction shunt position. An optimization procedure based on the predictions returned by the neural net is then executed in order to find the minimal number of correction shunts needed and their positions. During the experimental investigations, 98% of the deflection yokes analyzed have been tuned successfully using the technique proposed.