Name
Title | ||
|---|---|---|
Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction. |
Abstract | ||
|---|---|---|
Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17081830 | SENSORS |
Keywords | Field | DocType |
soft sensor,industrial blast furnace,silicon content,local learning,support vector regression,outlier detection | Data mining,Anomaly detection,Noisy data,Nonlinear system,Pattern recognition,Soft sensor,Support vector machine,Artificial intelligence,Predictive modelling,Engineering,Cluster analysis,Silicon | Journal |
Volume | Issue | ISSN |
17 | 8.0 | 1424-8220 |
Citations | PageRank | References |
2 | 0.39 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Chen | 1 | 16 | 8.01 |
| Yu Liang | 2 | 21 | 12.01 |
| Zengliang Gao | 3 | 3 | 1.43 |
| Yi Liu | 4 | 10 | 6.01 |