Convergence adjustment of deflection yoke using soft computing techniques

Won-Kyung Song, Joo-Han Kim, W. Bang, Sungwon Joo, Z. Bien, Sangbong Park
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引用次数: 7

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.
利用软计算技术进行偏转轭的收敛调整
高质量的偏转轭(DY)是高质量监视器的关键因素。目前,DY的调整过程是通过在DY的内表面附着铁氧体片来手动完成的。本文研究了制导系统中采用的收敛调整算法。提出了一种基于软计算技术的推理引擎,系统地处理包括专家知识在内的多种资源在DY的收敛调整过程中。该方法采用粗糙集理论处理DY调整的输入/输出数据集合,并采用模糊逻辑和进化规划对对象进行建模和模型调整。在给定初始收敛性的情况下,由粗糙集驱动的粗搜索部分找到一个粗区域,然后由模糊推理机驱动的细搜索部分搜索并确定更精确的位置。初步实验结果表明,该算法在实际的DY收敛调整过程中效果良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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