A successive learning neuro control system shooting irregular moving object

K. Hirota, T. Tsurumaru, A. Motegi, M. Ohtani, N. Yubazaki, T. Miyajima
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Abstract

A successive learning control system based on a neural network technique with a genetic algorithm has been developed to simulate a human real-time learning process. As an application experiment, a 3 degree-of-freedom arm robot shoots a ball equipped with a CCD camera at an irregular moving basket. Where the hitting rate is improved by the successive learning control and the final value was 23% in average, 40% in maximum.<>
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一种射击不规则运动物体的连续学习神经控制系统
为了模拟人的实时学习过程,提出了一种基于遗传算法的神经网络连续学习控制系统。作为一项应用实验,一个3自由度的手臂机器人在一个不规则移动的篮筐上发射一个装有CCD相机的球。其中,连续学习控制提高了命中率,最终命中率平均为23%,最大值为40%。
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