Deep Reinforcement Learning Apply in Electromyography Data Classification.pdf
文献类型：会议论文
| 作者 | Chengjie Song; Chenjie Chen; Yanjie Li; Xinyu Wu |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | shenzhen |
| 英文摘要 | In this paper, we try a novel approach to detect human movement intentions based on electromyography(EMG) signals. We use the Convolutional neural network(CNN) to extract EMG features automatically, then use dueling deep Q-learning, a reinforcement learning technique, to learn a classification policy which with an ability to select most helpful subset features and filter the irrelevant or redundant features from the deep learning features. We show that the deep learning method outperforms the multi-layer perceptron in the several subjects EMG data classification situation and the reinforcement networks can use less features to reach a relatively high classification precision |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13836]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Chengjie Song,Chenjie Chen,Yanjie Li,et al. Deep Reinforcement Learning Apply in Electromyography Data Classification.pdf[C]. 见:. shenzhen. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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