• **Training dataset**

In this study, the simulated data for the neural network model were divided into two subsets of training data and test data. Since there was no prior knowledge of the relative importance of unnatural patterns here, the training set contained approximately an equal number of training data for each type of pattern. In total, there were 11,000 training samples in the study set. The total number of training data in the LVQ network was 4000 samples, which equally considered 500 for each pattern. The total number of training samples for MLP networks was 7000, in which the same amount of 1000 samples was generated and applied for each of the seven MLP networks. In order to produce

the training dataset with the specifications mentioned above, a program was codified, which was capable of producing an unlimited number of natural and unnatural patterns with different parameters (for example, with different mean and standard deviations).


**Table 6.** Scaling range for outputs of MLPs.
