Equalization Optimizer-Based LSTM Application in Reservoir Identification

Table 7

Algorithm steps of the TAFEO-LSTM model.

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Algorithm: the TAFEO-LSTM model algorithm

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Inputs: Training set, test set, parameters of TAFEO

Output: TAFEO-LSTM model, test set labels, accuracy

1. Parameters for initializing the LSTM

2. Normalized data processing

3. Initializing the population

4. Calculate the fitness function and find the current optimal solution

5. While t < T

6.   For i = i: N

7.   Determine particle state and update particle position

8.   End for

9.   Updating the TAFEO-LSTM models to predict classification accuracy

10.   t = t + 1

11. End while

12. The optimal number of neurons with the hyperparameters batchsize and maxepoch is given to the TAFEO-LSTM model for retraining.

13. Building a TAFEO-LSTM model

14. Predictive classification of the test set

15. Calculate classification accuracy, AUC area, and draw ROC curves

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