| 1 |
Just-in-time learning based soft sensor with variable selection and weighting optimized by evolutionary optimization for quality prediction of nonlinear processes
Pan B, Jin HP, Wang L, Qian B, Chen XG, Huang S, Li JG
Chemical Engineering Research & Design, 144, 285, 2019 |
| 2 |
Soft Sensor Development for Nonlinear Industrial Processes Based on Ensemble Just-in-Time Extreme Learning Machine through Triple-Modal Perturbation and Evolutionary Multiobjective Optimization
Pan B, Jin HP, Yang B, Qian B, Zhao ZG
Industrial & Engineering Chemistry Research, 58(38), 17991, 2019 |
| 3 |
Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes
Jin HP, Chen XG, Yang JW, Zhang H, Wang L, Wu L
Chemical Engineering Science, 131, 282, 2015 |
| 4 |
Online prediction for contamination of chlortetracycline fermentation based on Dezert-Smarandache theory
Yang JW, Chen XG, Jin HP
Chinese Journal of Chemical Engineering, 23(6), 1009, 2015 |
| 5 |
Adaptive Soft Sensor Development Based on Online Ensemble Gaussian Process Regression for Nonlinear Time-Varying Batch Processes
Jin HP, Chen XG, Wang L, Yang K, Wu L
Industrial & Engineering Chemistry Research, 54(30), 7320, 2015 |
| 6 |
Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes
Jin HP, Chen XG, Yang JW, Wu L
Computers & Chemical Engineering, 71, 77, 2014 |
| 7 |
Cobaltoporphyrin-Catalyzed CO2/Epoxide Copolymerization: Selectivity Control by Molecular Design
Anderson CE, Vagin SI, Xia W, Jin HP, Rieger B
Macromolecules, 45(17), 6840, 2012 |
