Journal ArticleDOI
Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes
Yi Liu,Yi Liu,Junghui Chen +2 more
TLDR
An integrated nonlinear soft sensor modeling method is proposed for online quality prediction of multi-grade processes and the superiority of the proposed soft sensor is compared with other soft sensors in terms of online prediction of melt index in an industrial plant in Taiwan.About:
This article is published in Journal of Process Control.The article was published on 2013-07-01. It has received 130 citations till now. The article focuses on the topics: Soft sensor & Probabilistic analysis of algorithms.read more
Citations
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Journal ArticleDOI
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
TL;DR: The state-of-the-art of data mining and analytics are reviewed through eight unsupervisedLearning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms.
Journal ArticleDOI
A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes
TL;DR: This work aims to present a comprehensive review of the developments since the start of the millennium of soft sensing, from the perspective of systems and control.
Journal ArticleDOI
Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes
TL;DR: The industrial MI prediction results demonstrate the advantages of the developed EDKL model as compared with conventional supervised soft sensors that only use the limited labeled data.
Journal ArticleDOI
Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models
Weiming Shao,Xuemin Tian +1 more
TL;DR: In this article, a selective ensemble of local partial least squares models is proposed for quality prediction of nonlinear and time-varying chemical processes, where the process state is partitioned into local model regions upon which PLS models are constructed, through a statistical hypothesis testing based adaptive localization procedure.
Journal ArticleDOI
A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process
TL;DR: A layer-wise data augmentation (LWDA) strategy is proposed for the pretraining of deep learning networks and soft sensor modeling and the proposed LWDA-SAE model is applied to predict the 10% and 50% boiling points of the aviation kerosene in an industrial hydrocracking process.
References
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BookDOI
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
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Stastical Decision Theory and Bayesian Analysis.
Malay Ghosh,James O. Berger +1 more
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Statistical Decision Theory and Bayesian Analysis
TL;DR: An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.
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Least Squares Support Vector Machines
TL;DR: Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS- sVM for Unsupervised Learning LS- SVM for Recurrent Networks and Control.
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Locally Weighted Learning
TL;DR: The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, and applications of locally weighted learning.