2.2.3. Training Phase

A backpropagation approach is a simplified delta function for a feed-forward system with numerous layers that are used during the training phase. This is due to its ability to calculate the slope of each layer, continuously utilizing the chain principle. In practice, quadratic activation functions are utilized to improve performance due to their non-linearity and suitability with a feed-forward backpropagation training (FFBPT) approach. The LM was adopted as the learning classifier in this work because it is a rapid, simple, and stable approach. As a result, the FFBPT approach was chosen as the system structure for the ANN

design. The optimal ANN settings with the maximum precision, which is equivalent to R, were obtained by modifying the number of hidden layers, the number of neurons as well as the transfer function. In this work, a three-layer system with 10-hidden layers and a 1-output layer was adopted for both ANN models. While one hidden layer is sufficient for nonlinear modeling, a system with 2-hidden layers outperforms systems with 1- as well as 3-hidden layers in terms of the number of iterations, precision, and sophistication. Furthermore, the 3-layer system helps solve the challenge of slow learning rates.
