Name
Abstract | ||
|---|---|---|
Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal comp... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2016.2635111 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Principal component analysis,Feature extraction,Kernel,Monitoring,Fault detection,Fault diagnosis,Data models | Kernel (linear algebra),Data modeling,Nonlinear system,Subspace topology,Pattern recognition,Computer science,Fault detection and isolation,Kernel principal component analysis,Statistical model,Artificial intelligence,Principal component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 3 | 2162-237X |
Citations | PageRank | References |
7 | 0.47 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deng Xiaogang | 1 | 115 | 17.49 |
| Xuemin Tian | 2 | 71 | 7.54 |
| Sheng Chen | 3 | 1294 | 92.85 |
| Chris J. Harris | 4 | 700 | 66.65 |