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- W3147392885 abstract "最近，各种各样的控制方法由控制被用于的 proportional-integral-derivative (PID ) 代表了机器的控制。为高反应和精确应付要求，象严肃赔偿者， Coriolis/centrifugal 力量赔偿者和磨擦赔偿者那样的先进前馈控制控制器在控制器被造了。通常，当在一个短采样时期以内计算补偿价值时，它引起重计算负担。在这份报纸，综合周期性的神经网络为 PUMA560 操纵者作为一个前馈控制控制器被使用。前馈控制控制器工作而不是严肃和 Coriolis/centrifugal 力量赔偿者。在由使用背繁殖算法的神经网络的学习过程， S 字形的功能的学习系数和获得根据教信号直觉地并且以经验为主地被调节。因为它被尝试正在进行，调节是复杂的。特别，当教信号的规模大时，这个问题变得关键。应付使学习性能担心的问题，为教信号的大规模的一种简单、适应的学习技术被建议。学习技术和控制有效性用 PUMA560 操纵者的动态模型通过模拟被评估。" @default.
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- W3147392885 date "2011-01-01" @default.
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- W3147392885 title "Adaptive Learning with Large Variability of Teaching Signals for Neural Networks and Its Application to Motion Control of an Industrial Robot" @default.
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