Artificial neural network (ANN)

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.

• From the point of view of machine learning, neural networks are special cases of discriminant analysis, pattern recognition and grouping methods.
• From the mathematical point of view, the learning of neural networks is a multiparameter problem of nonlinear optimization.
• From the development of computation and programming, neural networks are a way to solve efficient parallel problems.
• From a cybernetic perspective, neural networks are used for problems of adaptive control and robotic algorithms.
• From the point of view of artificial intelligence, neural networks are the basis of the philosophy of connectionism and the main direction of the structural methods of using computer algorithms to establish the possibility of natural intelligence.

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.

Stages of problem solving

• Data collection for training;
• Selection of network topology;
• Preparation and normalization of data;
• Experimental selection of training parameters;
• Experimental selection of network characteristics;
• Actually learning;
• Checking the adequacy of training;
• Adjustment of parameters, final training;
• Verbalization of the network for further use.

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.