Application research of DHNN model in prediction of classification of rockburst intensity
In view of problems of randomness and subjectivity in determining weight of existing rockburst prediction methods，a discrete Hopfield neural network (DHNN) model for prediction of classification of rockburst intensity was proposed。The model selects stress coefficient, rockbrittleness coefficient and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2018-01-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2017050027 |
| _version_ | 1797868945821138944 |
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| author | XU Jia CHEN Junzhi LIU Chenyu WANG Jiaxin LONG Gang LI Chunyi |
| author_facet | XU Jia CHEN Junzhi LIU Chenyu WANG Jiaxin LONG Gang LI Chunyi |
| author_sort | XU Jia |
| collection | DOAJ |
| description | In view of problems of randomness and subjectivity in determining weight of existing rockburst prediction methods，a discrete Hopfield neural network (DHNN) model for prediction of classification of rockburst intensity was proposed。The model selects stress coefficient, rockbrittleness coefficient and elastic energy index as evaluation index, divides rockburst grade into 4 stages, such as strong rockburst, medium rockburst, weak rockburst and no rockburst, then encodes them. The model need't normalize sample data with simpler encoding ,lesser iterations of network and better associative memory ability, only be converted to '1' and '-1' of the two value model, therefore, the classification prediction of rockburst intensity is more scientific and reasonable. The model can provide a new way for classification prediction of rockburst intensity in deep underground engineering. The prediction results of typical rockburst engineering examples prove the correctness of the model. |
| first_indexed | 2024-04-10T00:03:49Z |
| format | Article |
| id | doaj.art-d4bca1fd967f4379822749f3eec5a444 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:03:49Z |
| publishDate | 2018-01-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-d4bca1fd967f4379822749f3eec5a4442023-03-17T01:20:15ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2018-01-01441848810.13272/j.issn.1671-251x.2017050027Application research of DHNN model in prediction of classification of rockburst intensityXU JiaCHEN JunzhiLIU ChenyuWANG JiaxinLONG GangLI ChunyiIn view of problems of randomness and subjectivity in determining weight of existing rockburst prediction methods，a discrete Hopfield neural network (DHNN) model for prediction of classification of rockburst intensity was proposed。The model selects stress coefficient, rockbrittleness coefficient and elastic energy index as evaluation index, divides rockburst grade into 4 stages, such as strong rockburst, medium rockburst, weak rockburst and no rockburst, then encodes them. The model need't normalize sample data with simpler encoding ,lesser iterations of network and better associative memory ability, only be converted to '1' and '-1' of the two value model, therefore, the classification prediction of rockburst intensity is more scientific and reasonable. The model can provide a new way for classification prediction of rockburst intensity in deep underground engineering. The prediction results of typical rockburst engineering examples prove the correctness of the model.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2017050027coal miningdeep underground engineeringrockburst intensityclassification predictionelastic energyrock brittleness coefficientdiscrete hopfield neural network |
| spellingShingle | XU Jia CHEN Junzhi LIU Chenyu WANG Jiaxin LONG Gang LI Chunyi Application research of DHNN model in prediction of classification of rockburst intensity Gong-kuang zidonghua coal mining deep underground engineering rockburst intensity classification prediction elastic energy rock brittleness coefficient discrete hopfield neural network |
| title | Application research of DHNN model in prediction of classification of rockburst intensity |
| title_full | Application research of DHNN model in prediction of classification of rockburst intensity |
| title_fullStr | Application research of DHNN model in prediction of classification of rockburst intensity |
| title_full_unstemmed | Application research of DHNN model in prediction of classification of rockburst intensity |
| title_short | Application research of DHNN model in prediction of classification of rockburst intensity |
| title_sort | application research of dhnn model in prediction of classification of rockburst intensity |
| topic | coal mining deep underground engineering rockburst intensity classification prediction elastic energy rock brittleness coefficient discrete hopfield neural network |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2017050027 |
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