[Retracted] Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network
Algorithm1
: The neural network testing procedure.
Input: feature D.
Output: hybrid recurrent neural model.
Initialize the hypermastigote, which include the number of iterations t, the learning rate L, the hypermastigote of the recurrent neural network ,, and the computational gradient of the model ;
(1)
i cycles from 1 to t;
(2)
j cycles from 1 to t;
(3)
Calculate the eigenvalues of each channel and substitute them into the function f;
(4)
If j = t, then terminate the loop and execute the step 1;
(5)
If j < t, go back to step 1;
(6)
Extracting convolutional features to obtain F;
(7)
Combine the F1 values and local model eigenvalues to obtain the probability values;
(8)
Get the current job match value;
(9)
Sort and output the final result;
(10)
If i < t, then return to step (2) and loop through the i process;
