Introducing the basics of neural networks

ANNs are numerical models developed with the aim of reproducing simple neural activities of the human brain, such as object identification and voice recognition. The structure of an ANN is composed of nodes that, similar to the neurons present in a human brain, are interconnected with each other through weighted connections, which reproduce the synapses between neurons.

The system output is updated until it iteratively converges via the connection weights. The information derived from experimental activities is used as input data and the result processed by the network is returned as an output. The input nodes represent the predictive variables, and the output neurons are represented by the dependent variables. We use the predictive variables to process the dependent variables.

ANNs are very versatile in simulating regression and classification problems. They can learn the process of working out the solution to a problem by analyzing...