Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing
In order to optimize the control strategy of virtual industrial manufacturing,the convolution neural network algorithm based on wolf swarm optimization is used to study the control of virtual industrial manufacturing.Firstly,according to the task and resource data of virtual industrial manufacturing...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2021-10-01
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| Series: | Jisuanji kexue |
| Subjects: | |
| Online Access: | http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-135.pdf |
| _version_ | 1828913997286473728 |
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| author | XIAO Shi-long, WU Di, TANG Chao-chen, SHEN Xian-hao, ZHANG De-yu |
| author_facet | XIAO Shi-long, WU Di, TANG Chao-chen, SHEN Xian-hao, ZHANG De-yu |
| author_sort | XIAO Shi-long, WU Di, TANG Chao-chen, SHEN Xian-hao, ZHANG De-yu |
| collection | DOAJ |
| description | In order to optimize the control strategy of virtual industrial manufacturing,the convolution neural network algorithm based on wolf swarm optimization is used to study the control of virtual industrial manufacturing.Firstly,according to the task and resource data of virtual industrial manufacturing,the task resource list is established,and the task resource list is sparse combined with the unit matrix to form the virtual manufacturing cell.Then,the convolution neural network virtual manufacturing control model is established,and the weight and offset are optimized by using wolf swarm algorithm.Finally,the average manufacturing time of all tasks is taken as the objective function and the manufacturing unit is trained and optimized.The virtual manu-facturing experiment of marine main engine shows that compared with the common control algorithm,the convolution neural network algorithm optimized by wolves can obtain better average manufacturing time by setting the pool size of convolution kernel reasonably.<br/> |
| first_indexed | 2024-12-13T19:47:18Z |
| format | Article |
| id | doaj.art-61cfadcfe8f84a238251dea8751a9fd9 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-13T19:47:18Z |
| publishDate | 2021-10-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-61cfadcfe8f84a238251dea8751a9fd92022-12-21T23:33:31ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2021-10-01481013513910.11896/jsjkx.200900183Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual ManufacturingXIAO Shi-long, WU Di, TANG Chao-chen, SHEN Xian-hao, ZHANG De-yu01 Shenyang Ligong University,Shenyang 110159,China<br/>2 College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China<br/>3 School of Telecommunications Engineering,Xidian University,Xi'an 710071,China<br/>4 Guangxi Key Laboratory of Embedded Technology and Intelligent System,Guilin University of Technology,Guilin,Guangxi 541004,China<br/>5 School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,ChinaIn order to optimize the control strategy of virtual industrial manufacturing,the convolution neural network algorithm based on wolf swarm optimization is used to study the control of virtual industrial manufacturing.Firstly,according to the task and resource data of virtual industrial manufacturing,the task resource list is established,and the task resource list is sparse combined with the unit matrix to form the virtual manufacturing cell.Then,the convolution neural network virtual manufacturing control model is established,and the weight and offset are optimized by using wolf swarm algorithm.Finally,the average manufacturing time of all tasks is taken as the objective function and the manufacturing unit is trained and optimized.The virtual manu-facturing experiment of marine main engine shows that compared with the common control algorithm,the convolution neural network algorithm optimized by wolves can obtain better average manufacturing time by setting the pool size of convolution kernel reasonably.<br/>http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-135.pdfvirtual industrial manufacturing|wolf swarm algorithm|convolution neural network|virtual manufacturing cell|average manufacturing time |
| spellingShingle | XIAO Shi-long, WU Di, TANG Chao-chen, SHEN Xian-hao, ZHANG De-yu Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing Jisuanji kexue virtual industrial manufacturing|wolf swarm algorithm|convolution neural network|virtual manufacturing cell|average manufacturing time |
| title | Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing |
| title_full | Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing |
| title_fullStr | Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing |
| title_full_unstemmed | Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing |
| title_short | Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing |
| title_sort | control application of wolf group optimization convolutional neural network in ship virtual manufacturing |
| topic | virtual industrial manufacturing|wolf swarm algorithm|convolution neural network|virtual manufacturing cell|average manufacturing time |
| url | http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-135.pdf |
| work_keys_str_mv | AT xiaoshilongwuditangchaochenshenxianhaozhangdeyu controlapplicationofwolfgroupoptimizationconvolutionalneuralnetworkinshipvirtualmanufacturing |