Issue 70

T. Pham-Bao et alii, Frattura ed Integrità Strutturale, 70 (2024) 55-70; DOI: 10.3221/IGF-ESIS.70.03

Figure 1: The simple architecture of three layers of FNN.

At the hidden layer, the value bj corresponds to selecting the largest derivative of the error function to initialise each neuron's value. This speeds up the process of minimising errors. Feedforward networks typically use the average squared error (MSE) as their error function. In terms of MSE, the output of the network is compared with the output of the target according to the following formula:

2

(y -y )

N  j=1

j

t

MSE=

(16)

N

where y j is the output of ANN, y t is the target output, and N is the number of samples. The MSE is a function that depends on the weight and bias. The rate of error improvement is determined by the derivative of the activation function.

Figure 2: The general diagram of the experimental model.

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