Gas concentration prediction model for fully mechanized coal mining face
In view of problems of gas concentration prediction method based on least squares support vector machine (LS-SVM) such as easy to fall into local optimal solution, low search efficiency and easy to occur premature convergence during parameter optimization process, a gas concentration prediction mode...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2018-12-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.17364 |
| _version_ | 1797868971453579264 |
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| author | LI Huan JIA Jia YANG Xiuyu SONG Chunru |
| author_facet | LI Huan JIA Jia YANG Xiuyu SONG Chunru |
| author_sort | LI Huan |
| collection | DOAJ |
| description | In view of problems of gas concentration prediction method based on least squares support vector machine (LS-SVM) such as easy to fall into local optimal solution, low search efficiency and easy to occur premature convergence during parameter optimization process, a gas concentration prediction model based on ACO-LS-SVM was proposed. Firstly, k-means clustering analysis is performed on collected large amount of gas data on fully mechanized coal mining face to reduce dimension. Then, improved ant colony algorithm is used to optimize penalty parameters and kernel function parameters of LS-SVM, and the optimized parameters are substituted into the LS-SVM model for regression prediction. The simulation results show that when absolute error threshold of gas concentration is 0.03%, 0.04%, 0.05%, the prediction accuracy of the gas concentration prediction model based on ACO-LS-SVM is about 95%, which is better than SVM model and LS-SVM model. |
| first_indexed | 2024-04-10T00:04:11Z |
| format | Article |
| id | doaj.art-2aca9a9c00694d0790e61bcb809dc22a |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:04:11Z |
| publishDate | 2018-12-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-2aca9a9c00694d0790e61bcb809dc22a2023-03-17T01:18:45ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2018-12-014412485310.13272/j.issn.1671-251x.17364Gas concentration prediction model for fully mechanized coal mining faceLI Huan0JIA Jia1YANG Xiuyu2SONG Chunru3School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008,ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008,ChinaWangjialing branch, Shanxi China Coal Huajin Energy Co., Ltd.,Yuncheng 043000, ChinaTongmei Guodian Tongxin Coal Mine, Datong 037000, ChinaIn view of problems of gas concentration prediction method based on least squares support vector machine (LS-SVM) such as easy to fall into local optimal solution, low search efficiency and easy to occur premature convergence during parameter optimization process, a gas concentration prediction model based on ACO-LS-SVM was proposed. Firstly, k-means clustering analysis is performed on collected large amount of gas data on fully mechanized coal mining face to reduce dimension. Then, improved ant colony algorithm is used to optimize penalty parameters and kernel function parameters of LS-SVM, and the optimized parameters are substituted into the LS-SVM model for regression prediction. The simulation results show that when absolute error threshold of gas concentration is 0.03%, 0.04%, 0.05%, the prediction accuracy of the gas concentration prediction model based on ACO-LS-SVM is about 95%, which is better than SVM model and LS-SVM model.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.17364fully mechanized coal mining facegas concentration predictionant colony algorithmleast squares support vector machinek-means cluster analysisparameter optimizationls-svmaco-ls-svm |
| spellingShingle | LI Huan JIA Jia YANG Xiuyu SONG Chunru Gas concentration prediction model for fully mechanized coal mining face Gong-kuang zidonghua fully mechanized coal mining face gas concentration prediction ant colony algorithm least squares support vector machine k-means cluster analysis parameter optimization ls-svm aco-ls-svm |
| title | Gas concentration prediction model for fully mechanized coal mining face |
| title_full | Gas concentration prediction model for fully mechanized coal mining face |
| title_fullStr | Gas concentration prediction model for fully mechanized coal mining face |
| title_full_unstemmed | Gas concentration prediction model for fully mechanized coal mining face |
| title_short | Gas concentration prediction model for fully mechanized coal mining face |
| title_sort | gas concentration prediction model for fully mechanized coal mining face |
| topic | fully mechanized coal mining face gas concentration prediction ant colony algorithm least squares support vector machine k-means cluster analysis parameter optimization ls-svm aco-ls-svm |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.17364 |
| work_keys_str_mv | AT lihuan gasconcentrationpredictionmodelforfullymechanizedcoalminingface AT jiajia gasconcentrationpredictionmodelforfullymechanizedcoalminingface AT yangxiuyu gasconcentrationpredictionmodelforfullymechanizedcoalminingface AT songchunru gasconcentrationpredictionmodelforfullymechanizedcoalminingface |