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A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction
Joint Authors
Du, Dangdang
Jia, Xiaoliang
Hao, Chaobo
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM) ensemble model with phase space reconstruction (PSR) and particle swarm optimization (PSO), is presented.
First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently.
Then, all the primary prediction results are integrated to produce the most appropriate prediction results by another particular LSSVM—a multiple kernel LSSVM, which reduces the dependence of modeling accuracy on kernel function and parameters.
Phase space reconstruction theory is applied to extract the chaotic characteristic of input data source and reconstruct the data sample, and particle swarm optimization algorithm is used to obtain the best LSSVM individual members.
A case study is employed to verify the effectiveness of presented model with real operation data of aero engine.
The results show that prediction accuracy of the proposed model improves obviously compared with other three models.
American Psychological Association (APA)
Du, Dangdang& Jia, Xiaoliang& Hao, Chaobo. 2016. A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1112205
Modern Language Association (MLA)
Du, Dangdang…[et al.]. A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction. Mathematical Problems in Engineering No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1112205
American Medical Association (AMA)
Du, Dangdang& Jia, Xiaoliang& Hao, Chaobo. A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1112205
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112205