臂带传感器与手部机器人控制的神经网络算法

2018 International Conference on Applied Engineering (ICAE) Pub Date : 2018-10-01 DOI:10.1109/INCAE.2018.8579153

Sumantri R Kurniawan, D. Pamungkas

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引用次数: 16

摘要

控制机械手可以采用几种方法;其中一种方法是采用肌电传感器和时域方法。在本研究中，Myo手臂传感器与神经网络算法相结合。传感器信号的均方根用于系统的学习。两个隐藏层的学习率为0.7。每层使用三个节点。结果表明，该系统能够实时控制机器人，延时约1S。此外，反向传播神经网络的前馈过程精度为92.68%。

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MYO Armband sensors and Neural Network Algorithm for Controlling Hand Robot

To control the robot hand can be used several methods; one of them is by using EMG sensor and time domain methods. In this study, Myo Arm Sensors combined with Neural Network algorithm are used. The Root Mean Square of the sensor signals is used to be learning by the system. The learning rate is 0.7 with two hidden layers. Each layer used three nodes. The results obtained that the system enabled to control robot real time a delay of around 1S. Moreover, the accuracy of the feedforward process in backpropagation Neural Network is 92.68%.

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2018 International Conference on Applied Engineering (ICAE)

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