.png){kind=icon}
Return
Few-shot learning on batch process modeling with imbalanced data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ces.2023.119560.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Batch processes in manufacturing industries often adapt new products to meet the changing market demands. Dynamic modeling with limited data for new products may lead to inaccurate results. One solution is to extract useful knowledge from historical production data that can be applied to the new grade. However, past historical data from different product grades are often imbalanced. In this study, a meta-learning subspace identification (meta-SID) scheme is proposed to quickly learn a model of the new grade. It can extract more robust common knowledge from historical batches with imbalanced datasets; then the extracted common parameters can be directly transferred into a subspace identification for a new grade of product. The new SID model can be quickly trained even with a limited number of batch data. A numerical example and a case of a real industrial polyvinyl chloride process demonstrate the effectiveness of the proposed algorithm.
Keywords:
Batch process
Few-shot learning
Imbalanced data
Meta-learning
Subspace identification
Journal
IF:
4.3
Papers:
2.3W
Citations:
5.5W
Organization
Cited Papers
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi -class imbalanced datasets using transfer learning.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAIShareSaveSupervised functional modeling method for long durations of batch processes with limited batch data
PREAI
ShareSaveShareSaveShareSaveQuality prediction for multi-grade batch process using sparse flexible clustered multi-task learning
PREAI
ShareSaveBeyond Trace Ratio: Weighted Harmonic Mean of Trace Ratios for Multiclass Discriminant Analysis
PREAI
ShareSave
View more