Patent ID: 11900255
Assignee: UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 5:
6. The method according to claim 1, wherein in the step S6, a warm-up restart random gradient descent method is applied in training the training and testing the LSTM neural network Si yield prediction model, the LSTM neural network Mn yield prediction model, and the LSTM neural network Cr yield prediction model to dynamically adjust a learning rate:
the learning rate adjustment is shown in equation (3):, η
      t
     
     =
     
      
       η
       min
       i
      
      +
      
       
        1
        2
       
       ⁢
       
        (
        
         
          η
          max
          i
         
         -
         
          η
          min
          i
         
        
        )
       
       ⁢
       
        (
        
         1
         +
         
          cos
          ⁡
          (
          
           
            
             T
             cur
            
            
             T
             i
            
           
           ⁢
           π
          
          )
         
        
        )
       
      
     
    
   
   
    
     (
     3
     )
    
   
  
 

where, ηmini and ηmaxi are the range of learning rate; Tcur represents the number of epoch changes from the beginning to the end of each restart; Ti represents the restart cycle.