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NAS

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Neural architecture search framework based on reinforcement learning:"A Novel Approach to Detecting Muscle Fatigue Based on sEMG by Using Neural Architecture Search Framework"

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# NAS based on hierarchical exploration reinforcement learning 
![Visitors](https://api.visitorbadge.io/api/visitors?path=https://github.com/Shurun-Wang/NAS&label=visitors&countColor=%232ccce4&style=plastic)  This is the official source code repository of "A Novel Approach to Detecting Muscle Fatigue Based on sEMG by Using Neural Architecture Search Framework".

This framework is used to seek for high-performance neural networks. You can directly run the "new2QNN_main.py" file. We use multiple sEMG signals as the data_source and classify the fatigue states. You can change it to your own datasets. The data originates from my doctoral dissertation and consists of 4-channel sEMG, which differs from the data in the journal article. However, the methodology and principles remain the same.

If our work is helpful to you, please **Star** it and kindly **Cite** our paper as:

@article{WangNovel,
      author={Wang, Shurun and Tang, Hao and Wang, Bin and Mo, Jia},
      journal={IEEE Transactions on Neural Networks and Learning Systems}, 
      title={A Novel Approach to Detecting Muscle Fatigue Based on sEMG by Using Neural Architecture Search Framework}, 
      year={2023},
      volume={34},
      number={8},
      pages={4932-4943},
      doi={10.1109/TNNLS.2021.3124330}}

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