.png){kind=icon}
返回
Product quality prediction method in small sample data environment
.png){kind=icon}
PRE
AI
DOI：10.1016/j.aei.2023.101975.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The low degree of enterprise digitization and the existence of personalized customization and small batch production manufacturing modes lead to the characteristics of small samples and high dimensions of data collected by manufacturing enterprises. The product quality prediction method based on big data is prone to overfitting in the small sample and high dimensional data environment, and the model generalization performance is poor. In order to solve the above problems, this paper conducts research in two aspects of data augmentation and model optimization respectively. At the data augmentation level, a data generation model RVAE-CGAN is proposed, RVAE constrains the value space of GAN and eliminates the influence of outliers in the original data on the quality of generated samples. Using DBSCAN to cluster raw data to generate conditional vectors for guiding sample generation, and finally obtain high-quality generated samples. At the model optimization level, a product quality prediction model PPO-SVR is proposed. The PPO method is used to optimize the hyperparameters of the SVR model and improve the prediction effect of the model. RVAE-CGAN and PPO-SVR models are combined, and RVAE-CGAN is used to expand the number of samples to train PPO-SVR. The experimental results show that the product quality prediction model constructed based on RVAE-CGAN and PPO-SVR outperforms the BP neural network prediction model trained on mixed data. Therefore, the combination of data augmentation and model optimization proposed in this paper can significantly improve the prediction effect of the product quality prediction model in the small sample data environment.
Keyword:
Small samples
Robust variational auto encoder
Conditional generative adversarial networks
Proximal policy optimization
Support vector regression
Quality prediction
期刊
IF:
9.9
论文数:
4.4K
被引数:
1.7W
机构
引用论文
Learning Deep Multimanifold Structure Feature Representation for Quality Prediction With an Industrial Application通过工业应用学习用于质量预测的深度多模型结构特征表示
PREAI
分享收藏分享收藏
Machine learning seismic reservoir prediction method based on virtual sample generation.png)
PETROLEUM SCIENCE
OAAI
PETROLEUM SCIENCEIF6.1
OAAI分享收藏A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process深度学习网络的分层数据增强策略及其在工业加氢裂化过程中的软测量应用
PREAI
分享收藏
查看更多内容