Name
Title | ||
|---|---|---|
Feature selection of frequency spectrum for modeling difficulty to measure process parameters |
Abstract | ||
|---|---|---|
Some difficulty to measure process parameters can be obtained using the vibration and acoustical frequency spectra. The dimension of the frequency spectrum is very large. This poses a difficulty in selecting effective frequency band for modeling. In this paper, the partial least squares (PLS) algorithm is used to analyze the sensitivity of the frequency spectrum to these parameters. A sphere criterion is used to select different frequency bands from vibration and acoustical spectrum. The soft sensor model is constructed using the selected vibration and acoustical frequency band. The results show that the proposed approach has higher accuracy and better predictive performance than existing approaches. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31362-2_10 | ISNN (2) |
Keywords | Field | DocType |
feature selection,selected vibration,acoustical frequency band,process parameter,different frequency band,frequency spectrum,acoustical spectrum,predictive performance,effective frequency band,acoustical frequency spectrum,higher accuracy,partial least squares,soft sensor | Pattern recognition,Feature selection,Computer science,Soft sensor,Frequency band,Partial least squares regression,Spectral line,Artificial intelligence,Vibration,Radio spectrum,Effective frequency | Conference |
Citations | PageRank | References |
1 | 0.37 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Tang | 1 | 526 | 148.30 |
| Lijie Zhao | 2 | 41 | 9.72 |
| Yi-miao Li | 3 | 1 | 0.37 |
| Tianyou Chai | 4 | 2014 | 175.55 |
| S. -Z. Qin | 5 | 85 | 16.26 |