111 related articles for article (PubMed ID: 29990211)
1. An Improved Mixture of Probabilistic PCA for Nonlinear Data-Driven Process Monitoring.
Zhang J; Chen H; Chen S; Hong X
IEEE Trans Cybern; 2019 Jan; 49(1):198-210. PubMed ID: 29990211
[TBL] [Abstract][Full Text] [Related]
2. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.
Deng X; Tian X; Chen S; Harris CJ
IEEE Trans Neural Netw Learn Syst; 2018 Mar; 29(3):560-572. PubMed ID: 28026785
[TBL] [Abstract][Full Text] [Related]
3. Health status monitoring for ICU patients based on locally weighted principal component analysis.
Ding Y; Ma X; Wang Y
Comput Methods Programs Biomed; 2018 Mar; 156():61-71. PubMed ID: 29428077
[TBL] [Abstract][Full Text] [Related]
4. Deep Probabilistic Principal Component Analysis for Process Monitoring.
Kong X; He Y; Song Z; Liu T; Ge Z
IEEE Trans Neural Netw Learn Syst; 2024 Apr; PP():. PubMed ID: 38652625
[TBL] [Abstract][Full Text] [Related]
5. Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring.
Deng X; Wang L
ISA Trans; 2018 Jan; 72():218-228. PubMed ID: 29017769
[TBL] [Abstract][Full Text] [Related]
6. Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.
Mansouri M; Nounou MN; Nounou HN
IEEE Trans Nanobioscience; 2017 Sep; 16(6):504-512. PubMed ID: 28708564
[TBL] [Abstract][Full Text] [Related]
7. An improved mixture robust probabilistic linear discriminant analyzer for fault classification.
Liu Y; Zeng J; Xie L; Lang X; Luo S; Su H
ISA Trans; 2020 Mar; 98():227-236. PubMed ID: 31466729
[TBL] [Abstract][Full Text] [Related]
8. A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process.
Onel M; Kieslich CA; Pistikopoulos EN
AIChE J; 2019 Mar; 65(3):992-1005. PubMed ID: 32377021
[TBL] [Abstract][Full Text] [Related]
9. Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis.
Cai L; Tian X; Chen S
IEEE Trans Neural Netw Learn Syst; 2017 Jan; 28(1):122-135. PubMed ID: 26685274
[TBL] [Abstract][Full Text] [Related]
10. Fault Detection and Isolation of Non-Gaussian and Nonlinear Processes Based on Statistics Pattern Analysis and the
Zhou Z; Wang J; Yang C; Wen C; Li Z
ACS Omega; 2022 Jun; 7(22):18623-18637. PubMed ID: 35694521
[TBL] [Abstract][Full Text] [Related]
11. Mixtures of probabilistic principal component analyzers.
Tipping ME; Bishop CM
Neural Comput; 1999 Feb; 11(2):443-82. PubMed ID: 9950739
[TBL] [Abstract][Full Text] [Related]
12. Global-and-local-structure-based neural network for fault detection.
Zhao H; Lai Z; Chen Y
Neural Netw; 2019 Oct; 118():43-53. PubMed ID: 31228723
[TBL] [Abstract][Full Text] [Related]
13. Probabilistic principal component analysis for metabolomic data.
Nyamundanda G; Brennan L; Gormley IC
BMC Bioinformatics; 2010 Nov; 11():571. PubMed ID: 21092268
[TBL] [Abstract][Full Text] [Related]
14. Moving window KPCA with reduced complexity for nonlinear dynamic process monitoring.
Jaffel I; Taouali O; Harkat MF; Messaoud H
ISA Trans; 2016 Sep; 64():184-192. PubMed ID: 27342996
[TBL] [Abstract][Full Text] [Related]
15. Neighborhood preserving neural network for fault detection.
Zhao H; Lai Z
Neural Netw; 2019 Jan; 109():6-18. PubMed ID: 30388431
[TBL] [Abstract][Full Text] [Related]
16. Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models.
Benameur S; Mignotte M; Destrempes F; De Guise JA
IEEE Trans Biomed Eng; 2005 Oct; 52(10):1713-28. PubMed ID: 16235657
[TBL] [Abstract][Full Text] [Related]
17. Novel variation mode decomposition integrated adaptive sparse principal component analysis and it application in fault diagnosis.
Geng Z; Duan X; Han Y; Liu F; Xu W
ISA Trans; 2022 Sep; 128(Pt B):21-31. PubMed ID: 34857354
[TBL] [Abstract][Full Text] [Related]
18. Mixture of Probabilistic Principal Component Analyzers for Shapes from Point Sets.
Gooya A; Lekadir K; Castro-Mateos I; Pozo JM; Frangi AF
IEEE Trans Pattern Anal Mach Intell; 2018 Apr; 40(4):891-904. PubMed ID: 28475045
[TBL] [Abstract][Full Text] [Related]
19. Probabilistic PCA self-organizing maps.
López-Rubio E; Ortiz-de-Lazcano-Lobato JM; López-Rodríguez D
IEEE Trans Neural Netw; 2009 Sep; 20(9):1474-89. PubMed ID: 19695998
[TBL] [Abstract][Full Text] [Related]
20. An improved approach for fault detection by simultaneous overcoming of high-dimensionality, autocorrelation, and time-variability.
Hajarian N; Movahedi Sobhani F; Sadjadi SJ
PLoS One; 2020; 15(12):e0243146. PubMed ID: 33332390
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]