Our brain is made up of millions of neurons that communicate through a process of electrical stimuli known as synapses. Artificial neural networks are models that allow us to simulate, in a simplified way, the processing of information in the way the human brain does in order to allow machines to make classifications and/or predictions.
The composition of artificial neural networks is based on a system of nodes. There are input nodes, internal relay nodes and output nodes. The input nodes are those that receive information from the outside, the internal transmission nodes are those that send information between nodes without having contact with the outside and the output nodes are responsible for emitting information to the outside.
The functioning of artificial neural networks consists of three stages: information input, information processing and subsequent output. The processing is known as learning and can be carried out by means of different behavioural algorithms that will define both the functioning of the connections and the final result, also known as output.
It should be noted that processing, or learning, can be carried out in three different ways depending on the presence of patterns and, once the network is input, the previously mentioned classifications and/or predictions can be obtained.
- Supervised processing has input and output patterns and, depending on this, the transmission nodes are modified to match the input to the output. Values corresponding to any input object may be predicted after obtaining a number of examples.
- Unsupervised processing provides only input patterns and lets the network classify the results based on common characteristics.
- Hybrid processing is characterised by the fact that it does not classify via target patterns. It is only told whether the output is correct or incorrect for a certain input pattern.
It is important to note that the concept of artificial neural networks is not a current concept since it emerged in the 1950s, but contemporary tools allow them to be carried out thanks to their great computing power, which was not present at the time the theory was developed. Today, anyone can develop an artificial neural network and train it from their computer, which was unthinkable not so long ago. This is why they are increasingly present in all kinds of industries.
Undoubtedly, artificial neural networks are one of the most important tools for processing large amounts of data and obtaining results from them. The technology allows us to save resources and, consequently, to reduce our costs. Industries that apply this set of tools will have a growing competitive advantage over those that do not.