Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines
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Weiming Shao | Zhihuan Song | Kai Wang | Zhiqiang Ge | Zhiqiang Ge | Weiming Shao | Kai Wang | Zhihuan Song
[1] Jianzhong Wang,et al. Adaptive multiple graph regularized semi-supervised extreme learning machine , 2018, Soft Comput..
[2] Zhiqiang Ge,et al. Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression , 2019, IEEE Transactions on Control Systems Technology.
[3] Zhiqiang Ge,et al. Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.
[4] Chuanhou Gao,et al. A comparative analysis of support vector machines and extreme learning machines , 2012, Neural Networks.
[5] Zhiqiang Ge,et al. Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application , 2018, IEEE Transactions on Industrial Electronics.
[6] Chunxia Zhang,et al. Generalized extreme learning machine autoencoder and a new deep neural network , 2017, Neurocomputing.
[7] Jie Chen,et al. A new Self-Organizing Extreme Learning Machine soft sensor model and its applications in complicated chemical processes , 2017, Eng. Appl. Artif. Intell..
[8] Sarthak Tiwari,et al. A deep learning based data driven soft sensor for bioprocesses , 2018, Biochemical Engineering Journal.
[9] José David Martín-Guerrero,et al. Regularized extreme learning machine for regression problems , 2011, Neurocomputing.
[10] Chao Yang,et al. Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes , 2018 .
[11] Danwei Wang,et al. Sparse Extreme Learning Machine for Classification , 2014, IEEE Transactions on Cybernetics.
[12] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[13] Zhiqiang Ge,et al. Semi-supervised mixture of latent factor analysis models with application to online key variable estimation , 2019 .
[14] Alessandro Chiuso,et al. The harmonic analysis of kernel functions , 2017, Autom..
[15] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[16] Luigi Fortuna,et al. Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .
[17] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[18] Shahaboddin Shamshirband,et al. Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters , 2018, Energies.
[19] Miao Zhang,et al. A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine , 2016 .
[20] Hongming Zhou,et al. Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[21] Biao Huang,et al. Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE , 2018, IEEE Transactions on Industrial Informatics.
[22] Dipankar Das,et al. Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining , 2013, IEEE Intelligent Systems.
[23] Shahaboddin Shamshirband,et al. Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network , 2018 .
[24] Zhiqiang Ge,et al. Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review , 2018, Industrial & Engineering Chemistry Research.
[25] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[26] Zhiqiang Ge,et al. Parallel Computing and SGD-Based DPMM For Soft Sensor Development With Large-Scale Semisupervised Data , 2019, IEEE Transactions on Industrial Electronics.
[27] S. Graziani,et al. A deep learning based soft sensor for a sour water stripping plant , 2017, 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).
[28] Rui Araújo,et al. Mixture of partial least squares experts and application in prediction settings with multiple operating modes , 2014 .
[29] Minxia Luo,et al. Outlier-robust extreme learning machine for regression problems , 2015, Neurocomputing.
[30] Cheng Wu,et al. Semi-Supervised and Unsupervised Extreme Learning Machines , 2014, IEEE Transactions on Cybernetics.
[31] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[32] Yu Tian,et al. A Framework and Modeling Method of Data-Driven Soft Sensors Based on Semisupervised Gaussian Regression , 2016 .
[33] Chao Wang,et al. Variational Bayesian extreme learning machine , 2014, Neural Computing and Applications.
[34] Biao Huang,et al. Multiple model based soft sensor development with irregular/missing process output measurement , 2011, 2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP).
[35] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[36] Guang-Bin Huang,et al. Trends in extreme learning machines: A review , 2015, Neural Networks.
[37] Yan-Lin He,et al. Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square , 2016 .
[38] Li Wang,et al. Dual learning-based online ensemble regression approach for adaptive soft sensor modeling of nonlinear time-varying processes , 2016 .
[39] Florian Steinke,et al. Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction , 2009, NIPS.
[40] Chee Kheong Siew,et al. Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.
[41] Di Tang,et al. A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application , 2017, IEEE Transactions on Industrial Electronics.
[42] Yan-Lin He,et al. Data driven soft sensor development for complex chemical processes using extreme learning machine , 2015 .
[43] Chu Zhang,et al. Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and AdaBoost-extreme learning machine , 2017 .
[44] Antonio J. Serrano,et al. BELM: Bayesian Extreme Learning Machine , 2011, IEEE Transactions on Neural Networks.
[45] Manuel Mucientes,et al. STAC: A web platform for the comparison of algorithms using statistical tests , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[46] Dexian Huang,et al. Data-driven soft sensor development based on deep learning technique , 2014 .
[47] Yan-Lin He,et al. Soft-sensing model development using PLSR-based dynamic extreme learning machine with an enhanced hidden layer , 2016 .