基于深度学习特征提取与多目标优化集成修剪的选择性集成学习软测量方法
作者:
作者单位:

1.昆明理工大学信息工程与自动化学院;2.北京理工大学化学与化工学院

作者简介:

通讯作者:

中图分类号:

TP273

基金项目:


Selective ensemble learning for soft sensor development based on deep learning for feature extraction and multi-objective optimization for ensemble pruning
Author:
Affiliation:

1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology.;2.School of Chemistry and Chemical Engineering, Beijing Institute of Technology

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    集成学习已成为一种广泛使用的软测量建模框架.但是,建立高性能的集成学习软测量模型依然面临特征选择不当、基模型多样性不足、基模型估计性能不佳等诸多挑战.为此,本文提出了一种基于堆栈自编码器多样性生成机制的选择性集成学习高斯过程回归(selective ensemble of stacked autoencoder based Gaussian process regression, SESAEGPR)软测量建模方法.该方法充分发挥深度学习在特征提取方面的优势,通过构建多样性的堆栈自编码器(stacked autoencoder, SAE)网络,建立基于隐特征的高斯过程回归(Gaussian process regression, GPR)基模型.随后,基于模型性能提升率和进化多目标优化对SAEGPR基模型实施两次集成修剪,以降低集成模型复杂度、保持甚至进一步提升模型估计性能.最终,引入PLS Stacking集成策略实现基模型融合.所提方法显著优于传统全局和全集成软测量建模方法,其有效性和优越性通过青霉素发酵过程和Tennessee Eastman化工过程获得了验证.

    Abstract:

    Ensemble learning has become a widely used soft sensor modeling framework. However, the establishment of high-performance ensemble learning soft sensor models still encounters many challenges such as improper feature selection, insufficient diversity of base models, and poor base model estimation performance. To this end, a selective ensemble of stacked autoencoder based Gaussian process regression (SESAEGPR) is proposed for soft sensor modeling. By fully utilizing the advantages of deep learning in feature extraction, the SESAEGPR method first builds a set of diverse stacked autoencoder (SAE) networks and then establishes a set of Gaussian process regression (GPR) models based on the already extracted latent features. Next, a two-stage ensemble pruning is performed. The first is achieved based on the model performance improvement, and the evolutionary multi-objective optimization approach is used for second. Ensemble pruning enables the reduction of ensemble model complexity while maintaining or even further improving the ensemble estimation performance. Finally, a PLS Stacking ensemble mechanism is employed to achieve the combination of the selected base models. The proposed method performs significantly better than the traditional global and fully integrated soft sensing methods, and its effectiveness and superiority have been verified through the penicillin fermentation process and the Tennessee Eastman chemical process.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-07-13
  • 最后修改日期:2021-11-26
  • 录用日期:2021-11-26
  • 在线发布日期: 2022-01-02
  • 出版日期: