Related references
Note: Only part of the references are listed.Soft sensor development based on improved just-in-time learning and relevant vector machine for batch processes
Jianlin Wang et al.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING (2021)
Double-Level Locally Weighted Extreme Learning Machine for Soft Sensor Modeling of Complex Nonlinear Industrial Processes
Xiangrui Zhang et al.
IEEE SENSORS JOURNAL (2021)
Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine
Hongbin Liu et al.
PROCESS BIOCHEMISTRY (2020)
Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures
Runzhao Li et al.
FUEL (2020)
Reliable prediction of n-heptane isomerization over Pt/(CrOx/ZrO2)-HMS via comparative assessment of regularization networks and surface response methodologies
Nastaran Parsafard et al.
RSC ADVANCES (2020)
Experimental optimization and reactor simulation of coal-derived naphtha reforming over Pt-Re/γ-Al2O3 using design of experiment and response surface methodology
Lisha Wang et al.
REACTION KINETICS MECHANISMS AND CATALYSIS (2018)
PET Viscosity Prediction Using JIT-based Extreme Learning Machine
Zhenxing Li et al.
IFAC PAPERSONLINE (2018)
Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: Application to complex chemical processes
Zhiqiang Geng et al.
ENERGY (2017)
Online Performance Monitoring and Modeling Paradigm Based on Just-in-Time Learning and Extreme Learning Machine for a Non-Gaussian Chemical Process
Xin Peng et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2017)
A hybrid statistical approach for modeling and optimization of RON: A comparative study and combined application of response surface methodology (RSM) and artificial neural network (ANN) based on design of experiment (DOE)
F. M. Elfghi
CHEMICAL ENGINEERING RESEARCH & DESIGN (2016)
Modeling the n-Hexane Isomerization over Iron Promoted Pt/WOx-ZrO2 Catalysts Using Artificial Neural Networks
Martha Leticia Hernandez-Pichardo et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2016)
Data driven soft sensor development for complex chemical processes using extreme learning machine
Yan-Lin He et al.
CHEMICAL ENGINEERING RESEARCH & DESIGN (2015)
Economic and Environmental Benefits of Higher-Octane Gasoline
Raymond L. Speth et al.
ENVIRONMENTAL SCIENCE & TECHNOLOGY (2014)
High reliability estimation of product quality using support vector regression and hybrid meta-heuristic algorithms
Saeid Shokri et al.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS (2014)
Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size
Hiromasa Kaneko et al.
COMPUTERS & CHEMICAL ENGINEERING (2013)
A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses
Jie Yu
COMPUTERS & CHEMICAL ENGINEERING (2012)
Multiway Gaussian Mixture Model Based Adaptive Kernel Partial Least Squares Regression Method for Soft Sensor Estimation and Reliable Quality Prediction of Nonlinear Multiphase Batch Processes
Jie Yu
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2012)
Review of adaptation mechanisms for data-driven soft sensors
Petr Kadlec et al.
COMPUTERS & CHEMICAL ENGINEERING (2011)
LIBSVM: A Library for Support Vector Machines
Chih-Chung Chang et al.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2011)
Artificial neural network models for HFCS isomerization process
Mehmet Yuceer
NEURAL COMPUTING & APPLICATIONS (2010)
Prediction of Pressure Drop of Slurry Flow in Pipeline by Hybrid Support Vector Regression and Genetic Algorithm Model
S. K. Lahiri et al.
CHINESE JOURNAL OF CHEMICAL ENGINEERING (2008)
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Qie He et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2007)
Real-time learning capability of neural networks
Guang-Bin Huang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2006)
Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst
S Nandi et al.
CHEMICAL ENGINEERING JOURNAL (2004)
A new data-based methodology for nonlinear process modeling
C Cheng et al.
CHEMICAL ENGINEERING SCIENCE (2004)
Support vector data description
DMJ Tax et al.
MACHINE LEARNING (2004)
Practical selection of SVM parameters and noise estimation for SVM regression
V Cherkassky et al.
NEURAL NETWORKS (2004)
A review of process fault detection and diagnosis Part III: Process history based methods
V Venkatasubramanian et al.
COMPUTERS & CHEMICAL ENGINEERING (2003)
Learning capability and storage capacity of two-hidden-layer feedforward networks
GB Huang
IEEE TRANSACTIONS ON NEURAL NETWORKS (2003)
Share Paper
