or regularities in the data. The goal of ANNs is to find a solution efficiently when algorithmic methods are computationally intensive or do not exist. An important advantage of ANNs is the ability to detect complex non-linear input-output relationships. For this reason, ANNs can be viewed as universal non-linear function approximators [@HORNIK:1989]. Employing ANNs for mapping complex non-linear input-output problems offers a significant advantage over conventional techniques, such as regression techniques, because ANNs do not require explicit mathematical functions.

ANNs are defined as computer algorithms that mimic the human brain, being inspired by biological neural systems. Similar to the human brain, ANNs can perform complex tasks, such as learning, memorization and generalization. They are capable of learning from experience, storing knowledge and then applying this knowledge to make predictions.

A biological neuron has a cell body, a nucleus, dendrites and an axon. Dendrites act as inputs, the axon propagates the signal and the interaction between neurons takes place at synapses. Each synapse has an associated weight. When a neuron ‘fires’, it sends an output through the axon and the synapse to another neuron. Each neuron then collects all the inputs coming from linked neurons and produces an output. The artificial 