Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine Regression
In order to carry out aircraft structural load safety monitoring and accumulate relevant structural load data for aircraft fatigue life assessment, it is necessary to establish aircraft structural load model related to flight parameters. For the nonlinear relationship between aircraft structural loa...
| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Advances in Aeronautical Science and Engineering
2020-10-01
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| Series: | Hangkong gongcheng jinzhan |
| Subjects: | |
| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2020008?st=article_issue |
| _version_ | 1797958219468898304 |
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| author | TANG Ning BAI Xue |
| author_facet | TANG Ning BAI Xue |
| author_sort | TANG Ning |
| collection | DOAJ |
| description | In order to carry out aircraft structural load safety monitoring and accumulate relevant structural load data for aircraft fatigue life assessment, it is necessary to establish aircraft structural load model related to flight parameters. For the nonlinear relationship between aircraft structural loads and flight parameters, the sequential minimal optimization (SMO) algorithm with improved stopping criterion and the particle swarm optimization algorithm are used to improve the support vector machine regression method, and the flight parameters involved in the modeling are selected by the method of flight dynamics analysis combined with the Pearson correlation coefficient. Taking the transonic pitching maneuver of an aircraft as an example, a structural shear model of a wing is established, and the modeling method is verified by simulation. The results show that the accuracy of improved support vector machine regression method is better than the original method. It is concluded that the improved support vector machine regression method can improve the accuracy and generalization ability of the established model. |
| first_indexed | 2024-04-11T00:16:59Z |
| format | Article |
| id | doaj.art-e735cbed2b334a53a259f71c22ddb407 |
| institution | Directory Open Access Journal |
| issn | 1674-8190 |
| language | zho |
| last_indexed | 2024-04-11T00:16:59Z |
| publishDate | 2020-10-01 |
| publisher | Editorial Department of Advances in Aeronautical Science and Engineering |
| record_format | Article |
| series | Hangkong gongcheng jinzhan |
| spelling | doaj.art-e735cbed2b334a53a259f71c22ddb4072023-01-09T01:54:59ZzhoEditorial Department of Advances in Aeronautical Science and EngineeringHangkong gongcheng jinzhan1674-81902020-10-0111569470010.16615/j.cnki.1674-8190.2020.05.01220200512Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine RegressionTANG Ning0BAI Xue1Aircraft Flight Test Technology Institute, Chinese Flight Test Establishment, Xi’an 710089, ChinaAircraft Flight Test Technology Institute, Chinese Flight Test Establishment, Xi’an 710089, ChinaIn order to carry out aircraft structural load safety monitoring and accumulate relevant structural load data for aircraft fatigue life assessment, it is necessary to establish aircraft structural load model related to flight parameters. For the nonlinear relationship between aircraft structural loads and flight parameters, the sequential minimal optimization (SMO) algorithm with improved stopping criterion and the particle swarm optimization algorithm are used to improve the support vector machine regression method, and the flight parameters involved in the modeling are selected by the method of flight dynamics analysis combined with the Pearson correlation coefficient. Taking the transonic pitching maneuver of an aircraft as an example, a structural shear model of a wing is established, and the modeling method is verified by simulation. The results show that the accuracy of improved support vector machine regression method is better than the original method. It is concluded that the improved support vector machine regression method can improve the accuracy and generalization ability of the established model.http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2020008?st=article_issueaircraft structural loadsupport vector regressionsmo algorithmparticle swarm optimization algorithm |
| spellingShingle | TANG Ning BAI Xue Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine Regression Hangkong gongcheng jinzhan aircraft structural load support vector regression smo algorithm particle swarm optimization algorithm |
| title | Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine Regression |
| title_full | Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine Regression |
| title_fullStr | Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine Regression |
| title_full_unstemmed | Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine Regression |
| title_short | Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine Regression |
| title_sort | nonlinear aircraft structure load model based on improved support vector machine regression |
| topic | aircraft structural load support vector regression smo algorithm particle swarm optimization algorithm |
| url | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2020008?st=article_issue |
| work_keys_str_mv | AT tangning nonlinearaircraftstructureloadmodelbasedonimprovedsupportvectormachineregression AT baixue nonlinearaircraftstructureloadmodelbasedonimprovedsupportvectormachineregression |