Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing - XIAO Shi-long, WU Di, TANG Chao-chen, SHEN Xian-hao, ZHANG De-yu (Jisuanji kexue, 2021)
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.