Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR)
Little work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a predict...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/15/9038 |
| _version_ | 1797587001691602944 |
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| author | Mengyang Zhang Xuyinglong Zhang Shan Gao Yujie Zhu |
| author_facet | Mengyang Zhang Xuyinglong Zhang Shan Gao Yujie Zhu |
| author_sort | Mengyang Zhang |
| collection | DOAJ |
| description | Little work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a prediction method based on the Improved Particle Swarm Algorithm (IPSO) and optimized Support Vector Machine Regression (SVR). Focusing on the problems of the too-fast convergence and low accuracy of the traditional particle swarm algorithm (PSO), the improved particle swarm algorithm (IPSO) is obtained by linearly decreasing the dynamic adjustments of inertia weight <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ω</mi></mrow></semantics></math></inline-formula>, self-learning factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>c</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></semantics></math></inline-formula>, and social factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>c</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></semantics></math></inline-formula>; then, the penalty parameter <i>C</i> and kernel function parameter <i>σ</i> of SVR are optimized by the IPSO algorithm, and the comfort prediction of IPSO-SVR is established. The prediction accuracy of IPSO-SVR was 94.00%, the root mean square error RMSE was 0.37, the mean absolute value error MAE was 0.32, and the goodness of fit R<sup>2</sup> was 0.92. The results show that the optimized IPSO-SVR prediction model can more accurately predict seat comfort under different angles and backrest tilt angles and can provide reference and research value for related industries. The results show that the optimized nonlinear prediction model of IPSO-SVR has higher accuracy, and its prediction method is feasible and generalizable, meaning it can provide a reliable basis for the prediction of seat comfort under different angles and backrest inclinations, as well as providing reference and research value for related industries. |
| first_indexed | 2024-03-11T00:31:05Z |
| format | Article |
| id | doaj.art-270aed211e52456a9502444f37d85f15 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:31:05Z |
| publishDate | 2023-08-01 |
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| spelling | doaj.art-270aed211e52456a9502444f37d85f152023-11-18T22:40:51ZengMDPI AGApplied Sciences2076-34172023-08-011315903810.3390/app13159038Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR)Mengyang Zhang0Xuyinglong Zhang1Shan Gao2Yujie Zhu3Mechanical and Electrical Engineering College, Northeast Forestry University, Harbin 150040, ChinaMaterials Science and Engineering College, Northeast Forestry University, Harbin 150040, ChinaMechanical and Electrical Engineering College, Northeast Forestry University, Harbin 150040, ChinaMechanical and Electrical Engineering College, Northeast Forestry University, Harbin 150040, ChinaLittle work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a prediction method based on the Improved Particle Swarm Algorithm (IPSO) and optimized Support Vector Machine Regression (SVR). Focusing on the problems of the too-fast convergence and low accuracy of the traditional particle swarm algorithm (PSO), the improved particle swarm algorithm (IPSO) is obtained by linearly decreasing the dynamic adjustments of inertia weight <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ω</mi></mrow></semantics></math></inline-formula>, self-learning factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>c</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></semantics></math></inline-formula>, and social factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>c</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></semantics></math></inline-formula>; then, the penalty parameter <i>C</i> and kernel function parameter <i>σ</i> of SVR are optimized by the IPSO algorithm, and the comfort prediction of IPSO-SVR is established. The prediction accuracy of IPSO-SVR was 94.00%, the root mean square error RMSE was 0.37, the mean absolute value error MAE was 0.32, and the goodness of fit R<sup>2</sup> was 0.92. The results show that the optimized IPSO-SVR prediction model can more accurately predict seat comfort under different angles and backrest tilt angles and can provide reference and research value for related industries. The results show that the optimized nonlinear prediction model of IPSO-SVR has higher accuracy, and its prediction method is feasible and generalizable, meaning it can provide a reliable basis for the prediction of seat comfort under different angles and backrest inclinations, as well as providing reference and research value for related industries.https://www.mdpi.com/2076-3417/13/15/9038comfortsupport vector machine regressionimproved particle swarm algorithmLASSO regressionpredictive model |
| spellingShingle | Mengyang Zhang Xuyinglong Zhang Shan Gao Yujie Zhu Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR) Applied Sciences comfort support vector machine regression improved particle swarm algorithm LASSO regression predictive model |
| title | Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR) |
| title_full | Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR) |
| title_fullStr | Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR) |
| title_full_unstemmed | Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR) |
| title_short | Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR) |
| title_sort | comfort study of general aviation pilot seats based on improved particle swam algorithm ipso and support vector machine regression svr |
| topic | comfort support vector machine regression improved particle swarm algorithm LASSO regression predictive model |
| url | https://www.mdpi.com/2076-3417/13/15/9038 |
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