Cargando…
Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference
[Image: see text] By considering autocorrelation among process data, canonical variate analysis (CVA) can noticeably enhance fault detection performance. To monitor nonlinear dynamic processes, a kernel CVA (KCVA) model was developed by performing CVA in the kernel space generated by kernel principa...
Autores principales: | Wang, Xuemei, Wu, Ping |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178625/ https://www.ncbi.nlm.nih.gov/pubmed/35694473 http://dx.doi.org/10.1021/acsomega.2c01892 |
Ejemplares similares
-
Grand Canonical Ensembles of Sparse Networks and Bayesian Inference
por: Bianconi, Ginestra
Publicado: (2022) -
Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects
por: Malem-Shinitski, Noa, et al.
Publicado: (2022) -
A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring
por: Cheng, Hongchao, et al.
Publicado: (2020) -
Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data
por: Yoshida, Kosuke, et al.
Publicado: (2017) -
Inferring pseudogene–MiRNA associations based on an ensemble learning framework with similarity kernel fusion
por: Fan, Chunyan, et al.
Publicado: (2023)