Input variables are standardized, while outliers are clipped. In the second phase, the neural net divides the data set into three subsets, one of which it will use for training, one for validation, and one for testing. As the network of connections is built between the layers, each connection is assigned a statistical weight. Input layers are stimulated by software algorithms to send signals to hidden layers. As a signal is sent through each connection, it is multiplied by the connection weight.
Stimulated neurons receive signals from preceding layers of neurons and from a bias neuron.
They sum these signals, subject this sum to a statistical transformation, and transmit the result to neurons in subsequent layers. The output of these neurons is a function of the net structure and the connection weights. When the signaled output value is subtracted from the actual data value, the residual is an error. Errors are fed back from the output to the hidden layers for corrective adjustment of the connection weights to minimize the sum of squared errors. Training of the net takes place through this iterative feedforward of signal and feedback of the errors until a convergence of target with actual values takes place.