Student’s-t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes
出版年份 2018 全文链接
标题
Student’s-t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes
作者
关键词
-
出版物
SENSORS
Volume 18, Issue 11, Pages 3968
出版商
MDPI AG
发表日期
2018-11-16
DOI
10.3390/s18113968
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Variational Bayesian Gaussian Mixture Regression for Soft Sensing Key Variables in Non-Gaussian Industrial Processes
- (2017) Jinlin Zhu et al. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
- Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development
- (2017) Weiming Shao et al. NEUROCOMPUTING
- Data Mining and Analytics in the Process Industry: The Role of Machine Learning
- (2017) Zhiqiang Ge et al. IEEE Access
- Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models
- (2015) Weiming Shao et al. CHEMICAL ENGINEERING RESEARCH & DESIGN
- Multimode process data modeling: A Dirichlet process mixture model based Bayesian robust factor analyzer approach
- (2015) Jinlin Zhu et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor
- (2015) Weiming Shao et al. CHINESE JOURNAL OF CHEMICAL ENGINEERING
- Quality-related prediction and monitoring of multi-mode processes using multiple PLS with application to an industrial hot strip mill
- (2015) Kaixiang Peng et al. NEUROCOMPUTING
- Asymmetric Mixture Model With Simultaneous Feature Selection and Model Detection
- (2015) Thanh Minh Nguyen et al. IEEE Transactions on Neural Networks and Learning Systems
- Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression
- (2014) Xiaofeng Yuan et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- Database monitoring index for adaptive soft sensors and the application to industrial process
- (2013) Hiromasa Kaneko et al. AICHE JOURNAL
- Mixture of partial least squares experts and application in prediction settings with multiple operating modes
- (2013) Francisco A.A. Souza et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- Image segmentation by a new weighted Student's t-mixture model
- (2013) Hui Zhang et al. IET Image Processing
- Review of Recent Research on Data-Based Process Monitoring
- (2013) Zhiqiang Ge et al. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
- Virtual Sensing Technology in Process Industries: Trends and Challenges Revealed by Recent Industrial Applications
- (2012) Manabu Kano et al. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
- Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation
- (2011) Thanh Minh Nguyen et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples
- (2010) Zhiqiang Ge et al. AICHE JOURNAL
- Review of adaptation mechanisms for data-driven soft sensors
- (2010) Petr Kadlec et al. COMPUTERS & CHEMICAL ENGINEERING
- The state of the art in chemical process control in Japan: Good practice and questionnaire survey
- (2010) Manabu Kano et al. JOURNAL OF PROCESS CONTROL
- Data-driven Soft Sensors in the process industry
- (2009) Petr Kadlec et al. COMPUTERS & CHEMICAL ENGINEERING
- ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process
- (2008) J.C.B. Gonzaga et al. COMPUTERS & CHEMICAL ENGINEERING
- The mixtures of Student’s t-distributions as a robust framework for rigid registration
- (2008) Demetrios Gerogiannis et al. IMAGE AND VISION COMPUTING
- Robust fuzzy clustering using mixtures of Student’s-t distributions
- (2008) Sotirios Chatzis et al. PATTERN RECOGNITION LETTERS
- Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry
- (2007) Manabu Kano et al. COMPUTERS & CHEMICAL ENGINEERING
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