Methods for Deflection Yokes Tuning

This paper presents a method and a system for deflection yoke tuning to correct the misconvergence of colours of a cathode ray tube. The misconvergence of colours is characterised by 18 distances measured between the primary colour beam traces of the same picture element. The vision system grabs the picture at nine control positions and the computer calculates the distances between the colours. The method proposed consists of two phases, namely learning and operating. In the learning phase, the radial basis function neural network is trained to learn a mapping: correction shunt position → changes in misconvergence. In the operating phase, the trained neural network is used to predict changes in misconvergence depending on a correction shunt position. The decision about DY tuning with correction shunts is based upon the results of those predictions. During the experimental investigations, 98% of the deflection yokes analysed have been tuned correctly using the technique proposed. The software developed is easily adapted for deflection yokes of different types.