]> 2026-04-04T22:36:31+02:00 Publications:Intelligent deflection yoke magnetic field tuning 500 en <p>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.</p> http://dx.doi.org/10.1023/B:JIMS.0000026566.63878.72 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:237420 286 3 Journal of Intelligent Manufacturing Journal Paper 275 Intelligent deflection yoke magnetic field tuning 15 2004 2016-09-30T20:42:01Z 2457662.3625116 Intelligent deflection yoke magnetic field tuning 500 0 plainlist list 1 [[:Publications:Intelligent deflection yoke magnetic field tuning]] Intelligent deflection yoke magnetic field tuning# QUERY72566c4dceb94c23da612373f7ec178a 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Intelligent deflection yoke magnetic field tuning# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Intelligent deflection yoke magnetic field tuning# ERRe24f17e090c98448f3de99d03182b7b6