Empirical design of neural networks
While using neural networks in regression problems (that include prediction), there is no fixed number of hidden neurons, so usually the solver chooses an arbitrary number of neurons and then varies it according to the results produced by the networks created. This procedure may be repeated a number of times until a network with a satisfying criterion is found.
Experiments can be made on the same training and test datasets, while varying other network parameters, such as learning rate, normalization, and the number of hidden units. The objective is to choose the neural network that presents the best performance from the experiments. The best performance is assigned to the network that presents ...