Training and testing an ANN model in MATLAB

In the previous section, we saw the architecture of an ANN. It imposes two layers, input and output, which cannot be altered. Consequently, the critical factor lies in the number of hidden layers we consider. The size of a neural network is defined by the number of hidden neurons. Determining the optimal size of the network remains an ongoing challenge, as no analytical solution has been discovered to date. One approach to tackle this problem is to employ a heuristic method: creating various networks with increasing complexity, using a subset of the training data, and monitoring the error on a validation subset simultaneously. After completing the training process, the network with the lowest validation error is chosen as the preferred one.

How to train an ANN

Let’s discuss the process of choosing the number of layers. The number of input nodes is fixed based on the number of features in the input data, while the number of output...