2.2.4. Testing Phase

An additional batch of data was used to evaluate the trained system. The trained ANN was used to mimic the response output of the additional data batch. The optimal-trained system demonstrates that the modeled output matched the desired output accurately. The efficiency of the trained system was determined utilizing the R correlation value.

Table 2 illustrates a comparative analysis of the ANN types when using the first proposed model dataset. These results justify the selection for the FFANN, which yielded an MSE of 0.324 and an R2 of 0.96431.

**Table 2.** Comparative analysis of the ANN types.


RBNN—Radial basis neural network, MPM—Multilayer perception model, RNN—Recurrent Neural network, MSE—Mean Square error, R2—correlation coefficient.
