*5.1. Comparison Study*

To decisive the accuracy, consistency, and repeatability of test results, neural network model verification was done. The NN model was verified by comparing the error of the NN algorithm and the error rate of the discriminant analysis (DA) method—a classification counterpart in the statistical approach [1]. The method of DA is to find a rule to separate two or more groups of observations from one another. The most important application of DA is classification. The output of the DA for the test dataset is presented in Table 10.


**Table 10.** Output of discriminant analysis (DA) for test dataset.

For example, in Table 10, the number 0 in the first row and the eighth column indicates that no "natural" pattern was mistakenly placed in the systematic patterns class. In the first row and the first column, the number 17 represents the number of patterns correctly assigned to the "natural" type. Furthermore, the value of 35.42 is the percentage correctly assigned to the "natural" class. In the first row and second column, 15 is the number of "natural" patterns that were mistakenly classified as "shift". The value of 31.25 is also the percentage of the "shift" pattern error in the "natural" class. The value of 31.25 is also the percentage of the "shift" pattern error in the "natural" class. Table 11 shows the errors for each class. This table lists the errors for each category in the "rate" line and the weight for each type in the "priors" row, while "total" (0.3325) indicates the total error of DA method for the test dataset. Tables 12 and 13 provide the output for the training dataset. As shown in the diagrams below (Figures 11 and 12), the NN outperformed the DA method in terms of performance and accuracy.

**Table 11.** DA error in the test dataset classification.



**Table 12.** Output of discriminant analysis (DA) for training dataset.

**Table 13.** DA error in the training dataset classification.


**Figure 11.** Comparison of DA and neural network (NN) error for each pattern in in test dataset.

**Figure 12.** (**a**) Comparison of DA and NN error in test dataset; (**b**) comparison of DA and NN error in training dataset.
