TY - GEN
T1 - Key data set selection algorithm based on PLS regression in industrial process
AU - Yang, Mingyang
AU - Yang, Xuebo
AU - Yang, Chengming
AU - Zhou, Hongpeng
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/21
Y1 - 2016/12/21
N2 - In this paper, based on the traditional Partial Least Squares(PLS) algorithm, a new way to select the most effective data sets for the PLS regression is proposed. The reason why we apply this new approach is that it could maintain or even surpass the original performance of control and diagnosis of a certain process while keep the data sets as less as possible to enhance the conciseness. The most significant advantage of the proposed data set selection method is that it identifies the data sets with the most typical characteristics of the group of data, excluding less informative data. Based on the ordinary PLS algorithm and with the improvement of conciseness, a better performance on prediction and fitting could be achieved. To achieve these goals, the ordinary and the improved PLS algorithm are introduced, after which a new method of data set selection is proposed. Specifically, the enhanced effectiveness of the proposed approach could be revealed by the simulation results of a numerical case.
AB - In this paper, based on the traditional Partial Least Squares(PLS) algorithm, a new way to select the most effective data sets for the PLS regression is proposed. The reason why we apply this new approach is that it could maintain or even surpass the original performance of control and diagnosis of a certain process while keep the data sets as less as possible to enhance the conciseness. The most significant advantage of the proposed data set selection method is that it identifies the data sets with the most typical characteristics of the group of data, excluding less informative data. Based on the ordinary PLS algorithm and with the improvement of conciseness, a better performance on prediction and fitting could be achieved. To achieve these goals, the ordinary and the improved PLS algorithm are introduced, after which a new method of data set selection is proposed. Specifically, the enhanced effectiveness of the proposed approach could be revealed by the simulation results of a numerical case.
KW - Classical PLS regression
KW - Data set selection
KW - Improved PLS regression
KW - Process control
UR - https://www.scopus.com/pages/publications/85010078878
U2 - 10.1109/IECON.2016.7793952
DO - 10.1109/IECON.2016.7793952
M3 - 会议稿件
AN - SCOPUS:85010078878
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 7179
EP - 7184
BT - Proceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society
PB - IEEE Computer Society
T2 - 42nd Conference of the Industrial Electronics Society, IECON 2016
Y2 - 24 October 2016 through 27 October 2016
ER -