Multi-modal hybrid modeling strategy based on Gaussian Mixture Variational Autoencoder and spatial–temporal attention: Application to industrial process prediction-Reference-Cited by-同舟云学术

Multi-modal hybrid modeling strategy based on Gaussian Mixture Variational Autoencoder and spatial–temporal attention: Application to industrial process prediction

Published:2024-01 Issue: Volume:244 Page:105029
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Peng Haifei,Long Jian,Huang Cheng,Wei Shibo,Ye Zhencheng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference45 articles.

1. Adaptive weighted hybrid modeling of hydrocracking process and its operational optimization;Song;Ind. Eng. Chem. Res.,2021

2. Data-driven modeling: concept, techniques, challenges and a case study;Habib,2021

3. A data-driven online multimodal identification method for industrial processes based on complex network;Dong;J. Control Autom. Electr. Syst.,2023

4. Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference;Jiang;Chemometr. Intell. Lab. Syst.,2014

5. An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries;Bhadriraju;Comput. Chem. Eng.,2023

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