RegGAN:一个虚拟样本生成网络,用于开发具有小数据的软传感器
1Department of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.
ACS omega
|February 12, 2024
概括
本研究介绍了一种新的生成对抗网络,用于创建虚拟样本,以提高化学生产中的软传感器性能. 当实际数据有限时,这种方法可以增强实时监控,将错误减少21%以上.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程监控 过程监控
- 人工智能的人工智能
背景情况:
- 化学生产中的关键质量变量往往缺乏在线测量能力.
- 软传感器对于实时监控至关重要,但需要广泛的标记数据进行开发.
- 由于时间和成本的限制,获得足够的软传感器标记数据是具有挑战性的.
研究的目的:
- 提出一种新的回归生成对抗网络 (GAN),用于生成虚拟样本.
- 为了应对在开发高性能软传感器时标记数据不足的挑战.
- 提高化学生产系统中软传感器的预测性能.
主要方法:
- 开发了一个回归GAN,学习辅助和目标变量之间的数据分布和映射.
- 包含一个权重权重的自动编码器,以提高生成模型训练的稳定性.
- 利用相似度测量算法选择相关的虚拟样本,将其整合到训练集中.
主要成果:
- 拟议的GAN有效地生成了与实际数据非常相似的虚拟样本.
- 拟议方法生成的虚拟样本与其他方法相比,与真实样本的接近性更高.
- 将虚拟样本集成到基于长期短期记忆 (LSTM) 的软传感器中,使得根平均平方误差减少了21.03%.
结论:
- 提出的生成方法成功地创建了有价值的虚拟样本,以增强有限的现实世界数据.
- 这些虚拟样本的整合大大提高了软传感器的预测准确度.
- 这种方法为改善化学生产过程实时监控提供了可行的解决方案.
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