Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 135-139.doi: 10.11896/jsjkx.200900183

• Artificial Intelligence • Previous Articles     Next Articles

Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing

XIAO Shi-long1, WU Di2, TANG Chao-chen3,4, SHEN Xian-hao4, ZHANG De-yu5   

  1. 1 Shenyang Ligong University,Shenyang 110159,China
    2 College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China
    3 School of Telecommunications Engineering,Xidian University,Xi'an 710071,China
    4 Guangxi Key Laboratory of Embedded Technology and Intelligent System,Guilin University of Technology,Guilin,Guangxi 541004,China
    5 School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,China
  • Received:2020-09-25 Revised:2021-03-10 Online:2021-10-15 Published:2021-10-18
  • About author:XIAO Shi-long,born in 1981,associate professor,master sapervisor.His main research interests include virtual reality and virtual simulation.
  • Supported by:
    National Natural Science Foundation of China(61741303),Key Project of the Natural Science Foundation of Liaoning Province(20180520038),Industry-University Cooperation Collaborative Education Project of the Ministry of Education(201802284028) and Guangxi Natural Science Foundation of China(2018GXNSFAA294061).

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Abstract: 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.

Key words: Average manufacturing time, Convolution neural network, Virtual industrial manufacturing, Virtual manufacturing cell, Wolf swarm algorithm

CLC Number: 

  • TP393
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