of each neuron if the loss function is minimal the best fitting set of parameters are found shown in box 11 then proceed to the next step in box 12 if not change function minimization algorithm and repeat computation in box 10 the outputs from box 10 are fed to the second layer of hidden neurons 6 7 as shown in box 12 where a new user specified regression model and nonlinear estimation method is computed to determine the relationship between the input and the individual neuron weights if the loss function is minimal the best fitting set of parameters are found as shown in box 13 then proceed to the next step in box 14 if not change function minimization algorithm and repeat computation in box 12 the output of box 12 is now fed to the neuron 8 of the output layer illustrated in box 14 where similar user specified computation of regression model is performed the efficiency of the neural network structure is examined by computation of the relationship between total output values with given target values as shown in box 15 an inspection of the scatterplot of the observed vs predicted values confirms the appropriateness of the models for the data used one would basically expect all points to fall on a straight line if the models were misspecified then this plot will indicate a non linear pattern the later will as shown in box 16 mean that one must modify the weights and the estimation procedures from the onset as shown in box 10 if however there is a linear relationship then the neural network learning phase is complete as shown in box 17 fig3 shows the application of the.