Deflection Yoke(DY) adjustment process is done manually by attaching ferrite sheets on the inside surface of the DY. Due to the complexities of this process there is a shortage of experts. As a consequence, a guidance system that can position the ferrite sheets to adjust the DY is highly desirable in the industrial field.
In this case, the experiences and knowledges of the expert so called mental model is most important for automation. But the mental model can have unuseful information for automation (Incomplete, Unscientific, Unstable, Parsimonious etc).
In this thesis, first, the convergence adjustment algorithm employed in this guidance system is dealt with. An inference engine based on fuzzy model is proposed to systematically deal with expert's knowledges in the convergence adjustment process of the DY. Second, change of the convergence by a ferrite sheet using harmonics method is mathematically analyzed. This mathematical analysis can complement errors of the mental model and can be used as a simulation tool for the experiment that data collection is difficult in.
It is shown via experiment and simulation that this algorithm is successfully applied for real system and mathematical analysis is reasonable in view of the change of the convergence.