Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model
In order to guarantee the precision of the parameters of the probability integral method (PIM), starting from optimizing input and improving algorithm an algorithm integrating the genetic algorithm (GA) and particle swarm optimization (PSO) was put forward to optimize the prediction model of BP neur...
| Auteurs principaux: | , , |
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| Format: | Article |
| Langue: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2021-01-01
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| Collection: | Tehnički Vjesnik |
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| Accès en ligne: | https://hrcak.srce.hr/file/365675 |
| _version_ | 1827282305258881024 |
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| author | Shenshen Chi Xuexiang Yu* Lei Wang |
| author_facet | Shenshen Chi Xuexiang Yu* Lei Wang |
| author_sort | Shenshen Chi |
| collection | DOAJ |
| description | In order to guarantee the precision of the parameters of the probability integral method (PIM), starting from optimizing input and improving algorithm an algorithm integrating the genetic algorithm (GA) and particle swarm optimization (PSO) was put forward to optimize the prediction model of BP neural network and the mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The measured data of 50 working faces were chosen as the training and testing sets to build the MIV-GP-BP model. The results showed that among the five parameters, the RMSE was between 0.0058 and 1.1575, the MaxRE of q, tanβ, b and θ was less than 5.42%, and the MeaRE was less than 2.81%. The RMSE of s/H did not exceed 0.0058, the MaxRE was less than 9.66% and the MeaRE was less than 4.31% (the parameters themselves were small). The optimized neural network model had higher prediction accuracy and stability. |
| first_indexed | 2024-04-24T09:15:57Z |
| format | Article |
| id | doaj.art-5cf9555912214ab8acbd11d67b51c99b |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:15:57Z |
| publishDate | 2021-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-5cf9555912214ab8acbd11d67b51c99b2024-04-15T16:48:07ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392021-01-0128116016810.17559/TV-20200429151307Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP ModelShenshen Chi0Xuexiang Yu*1Lei Wang21) School of Earth and Environment, Anhui University of Science and Technology 2) School of Geomatics, Anhui University of Science and Technology 3) Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology 4) Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and TechnologySchool of Geomatics, Anhui University of Science and Technology, Huainan, 232001, ChinaSchool of Geomatics, Anhui University of Science and Technology, Huainan, 232001, ChinaIn order to guarantee the precision of the parameters of the probability integral method (PIM), starting from optimizing input and improving algorithm an algorithm integrating the genetic algorithm (GA) and particle swarm optimization (PSO) was put forward to optimize the prediction model of BP neural network and the mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The measured data of 50 working faces were chosen as the training and testing sets to build the MIV-GP-BP model. The results showed that among the five parameters, the RMSE was between 0.0058 and 1.1575, the MaxRE of q, tanβ, b and θ was less than 5.42%, and the MeaRE was less than 2.81%. The RMSE of s/H did not exceed 0.0058, the MaxRE was less than 9.66% and the MeaRE was less than 4.31% (the parameters themselves were small). The optimized neural network model had higher prediction accuracy and stability.https://hrcak.srce.hr/file/365675BP neural networkMIV algorithmmining subsidenceoptimization algorithmPIMunderground mining |
| spellingShingle | Shenshen Chi Xuexiang Yu* Lei Wang Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model Tehnički Vjesnik BP neural network MIV algorithm mining subsidence optimization algorithm PIM underground mining |
| title | Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model |
| title_full | Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model |
| title_fullStr | Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model |
| title_full_unstemmed | Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model |
| title_short | Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model |
| title_sort | calculation method of probability integration method parameters based on miv gp bp model |
| topic | BP neural network MIV algorithm mining subsidence optimization algorithm PIM underground mining |
| url | https://hrcak.srce.hr/file/365675 |
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