Quantum statistic based semi-supervised learning approach for industrial soft sensor development
暂无分享,去创建一个
[1] Zhiqiang Ge,et al. Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.
[2] Dexian Huang,et al. Data-driven soft sensor development based on deep learning technique , 2014 .
[3] Zhiqiang Ge,et al. Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .
[4] Fan Miao,et al. Adaptive Gaussian Mixture Model-Based Relevant Sample Selection for JITL Soft Sensor Development , 2014 .
[5] Steven X. Ding,et al. A New Soft-Sensor-Based Process Monitoring Scheme Incorporating Infrequent KPI Measurements , 2015, IEEE Transactions on Industrial Electronics.
[6] Luis A. Aguirre,et al. Data-driven soft sensor of downhole pressure for a gas-lift oil well , 2014 .
[7] Zhiqiang Ge,et al. Mixture Bayesian Regularization of PCR Model and Soft Sensing Application , 2015, IEEE Transactions on Industrial Electronics.
[8] Jin Wang,et al. A reduced order soft sensor approach and its application to a continuous digester , 2011 .
[9] Pierantonio Facco,et al. Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process , 2009 .
[10] Zhiqiang Ge,et al. Robust supervised probabilistic principal component analysis model for soft sensing of key process variables , 2015 .
[11] Koji Tsuda,et al. A Quantum-Statistical-Mechanical Extension of Gaussian Mixture Model , 2008 .
[12] Rui Araújo,et al. Online Mixture of Univariate Linear Regression Models for Adaptive Soft Sensors , 2014, IEEE Transactions on Industrial Informatics.
[13] Zhiqiang Ge,et al. Robust semi-supervised mixture probabilistic principal component regression model development and application to soft sensors , 2015 .
[14] Manfred K. Warmuth,et al. Bayesian generalized probability calculus for density matrices , 2009, Machine Learning.
[15] Rosenbaum,et al. Quantum annealing of a disordered magnet , 1999, Science.
[16] Furong Gao,et al. Mixture probabilistic PCR model for soft sensing of multimode processes , 2011 .
[17] Furong Gao,et al. Review of Recent Research on Data-Based Process Monitoring , 2013 .
[18] Luigi Fortuna,et al. Soft sensors for product quality monitoring in debutanizer distillation columns , 2005 .
[19] Yiannis Demiris,et al. A Quantum-Statistical Approach Towards Robot Learning by Demonstration , 2012 .
[20] Bogdan Gabrys,et al. Local learning‐based adaptive soft sensor for catalyst activation prediction , 2011 .
[21] Zhiqiang Ge,et al. Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples , 2011 .
[22] Zhiqiang Ge,et al. Mixture semisupervised principal component regression model and soft sensor application , 2014 .
[23] Bogdan Gabrys,et al. Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..