一种基于局部迁移建模框架的软传感器建模方法
Bo Wang1, Shaowen Huang1, Hangfei Cai1
1Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种新的深度转移学习方法,用于Pichia pastoris发酵. 它通过调整模型以适应不同的数据条件来提高软传感器建模准确度,提高预测能力.
科学领域:
- 生物技术是生物技术.
- 机器学习 机器学习
- 工艺工程的过程工程.
背景情况:
- 由于多阶段特性和数据异质性,Pichia pastoris发酵在软传感器建模中提出了挑战.
- 现有的方法在动态发酵环境中难以实现低模型匹配精度和预测能力.
研究的目的:
- 开发一种使用深度转移学习 (DTL) 的新型软传感器建模方法,用于Pichia pastoris发酵.
- 解决数据分布异质性,提高多条件发酵过程中的预测准确性.
主要方法:
- 一个局部传输建模框架将发酵数据分成子源域,使用K-means集群.
- 深度神经网络 (DNN) 通过对预测模型改进的火虫算法进行了优化.
- 相关性分析确定了最相关的子源域,用于深度转移微调,以创建目标模型.
主要成果:
- 拟议的DTL方法有效地从发酵数据中提取局部特征信息.
- 实现了更高的预测准确度和更好的模型概括性能.
- 该方法在各种Pichia pastoris发酵场景中证明了软传感器建模的可行性.
结论:
- 这种新的软传感器建模方法显著改进了Pichia pastoris发酵的现有方法.
- 在生物过程监测中,DTL提供了一个强大的解决方案来处理数据异质性.
- 这项研究通过精确的软传感器开发,为优化发酵过程提供了宝贵的工具.
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