Artificial neural network (ANN) is a group of neuron nodes, interconnected with each other. Every circular node works as an artificial neuron that exchanges information with each other, similar to the functional principles of biological neural networks. The whole system is termed ANN.
The main advantage of artificial neural network implementation lies in the ability to perform complex mathematical modelling for emulation. This helps scientists to predict different processes and trends based on the visualisation of these models.
ANN is a simple system of processors (artificial neuron), in which neurons connect and interact reciprocally with each other. Each neuron periodically receives signals and sends them to others. By connecting to a reasonably large network of similar neurons, these single processors can perform quite complex actions.
Neural networks are not just programmed, but are also well trained. The possibility of ‘learning’ is one of the main advantages of ANN over traditional algorithms.
Technically, ‘learning’ is to find the coefficient connection between neurons. During this process, the neural network is able to detect complex dependencies between data input and output and perform generalization. This means that if training succeeds, the network can return the correct results based on data not provided in training samples and incomplete and / or "noisy" and partially distorted data. So, with the help of the ANN we can predict the most probable event. For the business environment, such operations are vital in the financial, medical, construction and any decision-making field.