@inproceedings{06b2a4e408374dd0b0e437697bce29eb,
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. {\textcopyright} 2012 Springer-Verlag.",
keywords = "feature selection, frequency spectrum, partial least squares, soft sensor",
author = "Jian Tang and Li-Jie Zhao and Yi-Miao Li and Tian-you Chai and Qin, {S. Joe}",
year = "2012",
month = jul,
doi = "10.1007/978-3-642-31362-2_10",
language = "English",
isbn = "978-3-642-31361-5",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Berlin Heidelberg",
pages = "82--91",
editor = "Jun Wang and Yen, {Gary G.} and Polycarpou, {Marios M.}",
booktitle = "Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings",
note = "9th International Symposium on Neural Networks, ISNN 2012 ; Conference date: 11-07-2012 Through 14-07-2012",
}