Convergence adjustment of deflection yoke using soft computing techniques

Abstract

A high quality deflection yoke (DY) is an essential factor for a high quality monitor. Currently, the DY adjustment process is done manually by attaching ferrite sheets on the inside surface of the DY. In this paper, we deal with the convergence adjustment algorithms employed in the guidance system. An inference engine that is based on soft computing techniques is proposed to systematically deal with several resources including the expert's knowledge in the convergence adjustment process of the DY. In our approach, the rough set theory is used to handle the input/output data collections of the DY adjustment, and the fuzzy logic and evolutionary programming are used to model the plant and to tune the model. With the given initial convergence, a rough set driven coarse search part finds a coarse area and then more precise positions are searched and decided in the fuzzy inference engine driven fine search part. Initial experimental results show that the proposed algorithm works well in the real convergence adjustment process of the DY.