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Semi-supervised learning for data-driven soft-sensing of biological and chemical processes
Erik Esche, Torben Talis, Joris Weigert, Gerardo Brand Rihm, Byungjun You, Christian Hoffmann, Jens‐Uwe Repke
Technische Universität Berlin
来源Chemical Engineering Science
年份2022
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学科主题
工程Fault Detection and Control Systems
Control Systems and Identification · Advanced Control Systems Optimization
参考文献 48
Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
被引 1,422Oliver Nelles · 2000
Recursive Gaussian Process Regression Model for Adaptive Quality Monitoring in Batch Processes
被引 26Le Zhou, Junghui Chen, Zhihuan Song · Mathematical Problems in Engineering · 2015
Semi-Supervised Learning
被引 96Xueyuan Zhou, Mikhail Belkin · Academic press library in signal processing · 2014
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引用本文 26
Architectures for neural networks as surrogates for dynamic systems in chemical engineering
被引 47Erik Esche, Joris Weigert, Gerardo Brand Rihm · Process Safety and Environmental Protection · 2021
Developing semi-supervised latent dynamic variational autoencoders to enhance prediction performance of product quality
被引 28Yi Shan Lee, Junghui Chen · Chemical Engineering Science · 2022
Laplacian regularization of linear regression model for semi-supervised industrial soft sensor development
被引 25Junhua Zheng, Lingjian Ye, Zhiqiang Ge · Expert Systems with Applications · 2024
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