Prediction Research on Irregularly Cavitied Components Volume Based on Gray Correlation and PSO-SVM
The use of a micro-compressed air-volume-detection method to detect the volume of irregularly cavitied components has the characteristics of multi-variable coupling and nonlinearity. To solve this problem, a volume-prediction model of irregularly cavitied components based on gray correlation and a p...
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1354 |
| _version_ | 1797625173029945344 |
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| author | Xin Zhang Yueqiu Jiang Wei Zhong |
| author_facet | Xin Zhang Yueqiu Jiang Wei Zhong |
| author_sort | Xin Zhang |
| collection | DOAJ |
| description | The use of a micro-compressed air-volume-detection method to detect the volume of irregularly cavitied components has the characteristics of multi-variable coupling and nonlinearity. To solve this problem, a volume-prediction model of irregularly cavitied components based on gray correlation and a particle-swarm-optimization support-vector machine is proposed. In this paper, the gray-correlation method was used to extract the detection parameters that have the greatest correlation with the cavity volume. On the basis of the obtained detection parameters, the SVM algorithm was used to build an irregularly cavitied components volume-prediction model. During model training, since the regression accuracy and generalization performance of the SVM model depend on the proper setting of its two parameters (the penalty-parameter C and the kernel-parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>), and especially on the interaction of the parameters, this paper presents an optimal-selection approach towards the SVM parameters, based on the particle-swarm-optimization (PSO) algorithm. Experiments showed that the prediction model can better predict the volume of irregularly cavitied components, and the prediction accuracy was high, which played a guiding role in intellectual nondestructive testing of the volume of the irregularly cavitied components. |
| first_indexed | 2024-03-11T09:52:48Z |
| format | Article |
| id | doaj.art-286eb6473b03404887ecc1694931e70c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:52:48Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-286eb6473b03404887ecc1694931e70c2023-11-16T16:04:15ZengMDPI AGApplied Sciences2076-34172023-01-01133135410.3390/app13031354Prediction Research on Irregularly Cavitied Components Volume Based on Gray Correlation and PSO-SVMXin Zhang0Yueqiu Jiang1Wei Zhong2School of Automobile and Traffic, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaGraduate School, Shenyang Ligong University, Shenyang 110159, ChinaThe use of a micro-compressed air-volume-detection method to detect the volume of irregularly cavitied components has the characteristics of multi-variable coupling and nonlinearity. To solve this problem, a volume-prediction model of irregularly cavitied components based on gray correlation and a particle-swarm-optimization support-vector machine is proposed. In this paper, the gray-correlation method was used to extract the detection parameters that have the greatest correlation with the cavity volume. On the basis of the obtained detection parameters, the SVM algorithm was used to build an irregularly cavitied components volume-prediction model. During model training, since the regression accuracy and generalization performance of the SVM model depend on the proper setting of its two parameters (the penalty-parameter C and the kernel-parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>), and especially on the interaction of the parameters, this paper presents an optimal-selection approach towards the SVM parameters, based on the particle-swarm-optimization (PSO) algorithm. Experiments showed that the prediction model can better predict the volume of irregularly cavitied components, and the prediction accuracy was high, which played a guiding role in intellectual nondestructive testing of the volume of the irregularly cavitied components.https://www.mdpi.com/2076-3417/13/3/1354volume-detectionmicro-compressed airgray-correlation analysissupport vector machine |
| spellingShingle | Xin Zhang Yueqiu Jiang Wei Zhong Prediction Research on Irregularly Cavitied Components Volume Based on Gray Correlation and PSO-SVM Applied Sciences volume-detection micro-compressed air gray-correlation analysis support vector machine |
| title | Prediction Research on Irregularly Cavitied Components Volume Based on Gray Correlation and PSO-SVM |
| title_full | Prediction Research on Irregularly Cavitied Components Volume Based on Gray Correlation and PSO-SVM |
| title_fullStr | Prediction Research on Irregularly Cavitied Components Volume Based on Gray Correlation and PSO-SVM |
| title_full_unstemmed | Prediction Research on Irregularly Cavitied Components Volume Based on Gray Correlation and PSO-SVM |
| title_short | Prediction Research on Irregularly Cavitied Components Volume Based on Gray Correlation and PSO-SVM |
| title_sort | prediction research on irregularly cavitied components volume based on gray correlation and pso svm |
| topic | volume-detection micro-compressed air gray-correlation analysis support vector machine |
| url | https://www.mdpi.com/2076-3417/13/3/1354 |
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