Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Trend Rise Technology, Taichung 40842, Taiwan.
Sensors (Basel). 2018 Jun 28;18(7):2070. doi: 10.3390/s18072070.
An on-line machine learning approach integrating the genetic algorithm (GA) and jitter measurements is proposed to learn the write strategy for the infrared diode of ultra-speed CD-RW recorders. The recording performance differs significantly for the CD-RW discs recorded for the first, second, or third time above. It is difficult to learn one set of write strategy parameters for the infrared diode of ultra-speed CD-RW recorder that satisfies the recording specifications for three different types of discs. The GA is applied to the on-line learning of write strategy. However, the convergence of GA stagnates at the final stage of the learning process due to the fact that the write strategy parameters learned by the GA need to satisfy the recording specifications for discs recorded for the first time, second time and third time within one recording trial. To overcome this difficulty, a scheme called dynamic parameter encoding is proposed. This scheme improves the GA convergence and explores the search space much better than the conventional GA.
提出了一种在线机器学习方法，该方法将遗传算法 (GA) 和抖动测量集成在一起，用于学习超高速 CD-RW 记录器的红外二极管的写入策略。对于首次、第二次或第三次记录的 CD-RW 光盘，其记录性能有很大差异。对于超高速 CD-RW 记录器的红外二极管，很难学习一组满足三种不同类型光盘记录规范的写入策略参数。GA 被应用于在线学习写入策略。然而，由于 GA 学习的写入策略参数需要在一次记录尝试中满足首次、第二次和第三次记录的光盘的记录规范，因此 GA 在学习过程的最后阶段会停滞不前。为了克服这个困难，提出了一种称为动态参数编码的方案。该方案比传统的 GA 更好地提高了 GA 的收敛性并探索了搜索空间。
