基于样本平衡策略的多源迁移学习方法及其在乙烯精馏塔质量指标预测的应用

基于数据驱动的工业过程建模需要依赖大量的标记良好的数据集，但与目标任务直接相关的标注数据往往是有限的。因此，可以利用与其具有相关性的辅助训练数据进行建模以实现任务迁移。然而，样本的不平衡问题一定程度上影响了迁移学习的性能表现。因此，提出了一种基于样本平衡策略的多源迁移学习方法，首先，对于同一源域内样本，采用最小二乘方法融合多个候选预测器得到单个源预测器，以协同利用域内不同样本包含的可迁移信息。此外，对于不同源域间样本，基于误差函数将多个源预测器加权组合得到多源预测模型。最后以乙烯精馏塔为对象进行案例分析，验证了所提出方法的有效性。

Multisource Transfer Learning Method Based on Sample Balance Strategy and Its Application to Quality Index Prediction of Ethylene Distillation Column

Data-driven industrial process modeling needs to rely on a large number of well-labeled data sets, but the labeled data directly related to the target task is often limited. Therefore, the auxiliary training data with correlation can be used for modeling to achieve task transfer. However, the imbalance of samples affects the performance of transfer learning to a certain extent. Therefore, We propose a multi-source transfer learning method based on sample balance strategy. First, for the samples in the same source domain, using the least squares method to build a single source predictor by fusing multiple candidate predictors, so that the transferable information of different samples in the same domain can be synergized. In addition, for samples among different source domains, a multi-source prediction model is obtained by weighting multiple source predictors based on the error function. Finally, the case study of ethylene distillation column verifies the effectiveness of the proposed method.