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使用基于记忆的循环神经网络在FPGA上实现的颗粒污染物的建模.

Julio Alberto Ramírez-Montañez1, Jose de Jesús Rangel-Magdaleno2, Marco Antonio Aceves-Fernández1

  • 1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico.

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概括
此摘要是机器生成的。

这项研究开发了一个LSTM神经网络,用于预测二氧化和颗粒物等空气污染物. FPGA的实施实现了11%的改进,保持了24小时和72小时预测的准确性.

关键词:
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科学领域:

  • 环境科学 环境科学
  • 计算机工程 计算机工程
  • 人工智能的人工智能

背景情况:

  • 准确的空气质量评估对于保护生态系统和人类健康至关重要.
  • 标准空气污染物包括二氧化 (NO2),一氧化碳 (CO) 和颗粒物 (PM10,PM2.5).
  • 过度接触这些污染物会带来很大的风险.

研究的目的:

  • 在FPGA板上训练和实施一个长短记忆 (LSTM) 神经网络,用于空气污染物预测.
  • 在资源有限的系统上评估修改后的LSTM架构的性能.
  • 评估将预测模型集成到实时空气质量监测嵌入式系统中的可行性.

主要方法:

  • 开发和培训一个LSTM神经网络模型.
  • 在现场可编程门阵列 (FPGA) 板上实现训练模型.
  • 对FPGA实施的模型与原来的LSTM网络进行比较分析.

主要成果:

  • 与原始模型相比,FPGA实现的LSTM网络表现出11%的性能改善.
  • 修改后的架构尽管减少了神经元,但仍然保持了预测准确性.
  • 对于24小时和72小时的时间框架,都实现了准确的预测.
  • 将预测网络集成到像FPGAs这样的有限系统中的可行性得到了确认.

结论:

  • 在FPGA板上提出的LSTM网络为空气污染物建模和预测提供了有效的解决方案.
  • 这种方法可以在嵌入式系统中部署先进的AI模型,而不会影响准确性.
  • 这些发现突出了加强空气质量监测系统和进一步优化模型的潜力.