What is it, and what applications does an artificial neural network have?
- They have acquired great importance because they allow us to find solutions to complex problems using regular rule-based programming.
- Its applications have revolutionized the world of robotics and data algorithms.
- Although they are a trend, we must remember that in most cases, the improvements they produce compared to more straightforward methodologies do not justify their use.
What are artificial neural networks?
An artificial neural network, or for its acronym in English (ANN, Artificial Neural Network,) is a series of algorithms that look for relationships in a data set. It consists of interconnected nodes that give it the appearance of a biological neural network and from which it takes its name (despite the lack of consensus on how it reflects the functioning of the human brain).
The architecture of an artificial neural network
The architecture of these systems is made up of different layers of nodes. The most common structure usually has three layers of nodes interconnected with each other.
- Standard artificial neural network. Source: https://www.dspguide.com/
The first layer or input layer (input layer) has input nodes that send data to a second layer. These nodes are passive and pass the information on to the next layer. The number of nodes in this layer matches the amount of data entered.
The nodes of the second layer or hidden layer (hidden layer) filter the relevant patterns from those that are not, identifying the critical information. These nodes are active, which means that they combine the data coming from the previous layer. Each input received is multiplied by a weight ,and the results are added together and delimited with a function (sigmoid or logistic) to improve efficiency. These nodes usually represent 10% of those in the first layer.
In the third layer, also called the output layer, the second process is repeated, and the data is combined and modified again in the active nodes to produce the output values.
- Active node of an artificial neural network.
Advantages of the artificial neural network
Its ability to complete tasks with infinite combinations makes it ideal considering the growing trend of Big Data-based applications.
However, its unique ability to make sense of incomplete, ambiguous, or contradictory data makes it truly valuable. That is the ability to use controlled processes when there is no exact model to follow.
Types of neural networks
In example 1, we showed the operation of networks in a three-layer architecture and a single flow of information. However, a neural network can have infinite layers, nodes, and structures with more complex information fdatag rise to different types of artificial neural networks.
What is a neural network used for?
These algorithmic systems that help us solve problems have multiple applications that can be included in:
- Event prediction and simulations: Production of expected output values based on incoming data.
- Recognition and classification: Association of patterns and organization of data sets into predefined classes. Even identifying unique characteristics without previous data.
- Data processing and modeling: Validation, aggregation and analysis of data. Design and troubleshooting in complex software systems.
- Control engineering: Monitoring of computer systems and manipulation of robots. Including the creation of autonomous systems and robots.
- Artificial Intelligence: Being part of deep learning and machine learning technologies that are fundamental parts of artificial intelligence
When it makes sense to use a neural network and when it doesn’t
It is necessary to know the advanced methodologies, but at the same time we have to be efficient in our data projects. Under the premise of the parsimony principle, a simple methodology for a specific problem provides us with a fallible model, but practical in most cases.
In this article we have discussed the benefits of artificial neural networks that can undoubtedly add value to data modelling. However, the reality is that in 80% of data problems, neural networks do not produce a better result than from traditional models.
Therefore, we do not get carried away by fashion in the sector and let us be critical when analyzing a project and assessing the appropriate methodology with which to approach it. In this sense, ANNs have their advantage over traditional models in large volumes of data with many cases.
