Fig. 2. General ANN structure representation.
demonstrated in Fig. 1, in which the left column is the input layer, the right most column is the output layer, and in between the input and output layers is one hidden layer; since the efficiency and accuracy of this topology is sufficient for the present application. In each layer there are several Processing Elements (PE) (also called ‘neurons’). Fig. 1 also shows that the number of PEs in the input layer is equal to the number of input variables, while the number in the output layer is equal to the number of output variables. Te architecture selected for the topology
representation of the actual ANN model is that of a generalised feed-forward neural network, with only one hidden layer, and undergoes supervised training; Tat is, the data provided to the network are represented through the input layer, they are re-presented in the hidden layer for their features and
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In-depth
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