Reconstruct the Support Vectors to Improve LSSVM Sparseness for Mill Load Prediction
Joint Authors
Si, Gangquan
Zhang, Yanbin
Shi, Jianquan
Guo, Zhang
Jia, Lixin
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The sparse strategy plays a significant role in the application of the least square support vector machine (LSSVM), to alleviate the condition that the solution of LSSVM is lacking sparseness and robustness.
In this paper, a sparse method using reconstructed support vectors is proposed, which has also been successfully applied to mill load prediction.
Different from other sparse algorithms, it no longer selects the support vectors from training data set according to the ranked contributions for optimization of LSSVM.
Instead, the reconstructed data is obtained first based on the initial model with all training data.
Then, select support vectors from reconstructed data set according to the location information of density clustering in training data set, and the process of selecting is terminated after traversing the total training data set.
Finally, the training model could be built based on the optimal reconstructed support vectors and the hyperparameter tuned subsequently.
What is more, the paper puts forward a supplemental algorithm to subtract the redundancy support vectors of previous model.
Lots of experiments on synthetic data sets, benchmark data sets, and mill load data sets are carried out, and the results illustrate the effectiveness of the proposed sparse method for LSSVM.
American Psychological Association (APA)
Si, Gangquan& Shi, Jianquan& Guo, Zhang& Jia, Lixin& Zhang, Yanbin. 2017. Reconstruct the Support Vectors to Improve LSSVM Sparseness for Mill Load Prediction. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1190359
Modern Language Association (MLA)
Si, Gangquan…[et al.]. Reconstruct the Support Vectors to Improve LSSVM Sparseness for Mill Load Prediction. Mathematical Problems in Engineering No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1190359
American Medical Association (AMA)
Si, Gangquan& Shi, Jianquan& Guo, Zhang& Jia, Lixin& Zhang, Yanbin. Reconstruct the Support Vectors to Improve LSSVM Sparseness for Mill Load Prediction. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1190359
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190359