Research on the artificial neural network efficiency at low volumes of the training sample

The effectiveness of the binary artificial neural network at low volume of training samples was investigated. The studies were conducted with the MATLAB software’s Neural Network Toolbox module used for the classification task. Feed-forward artificial neural network was thoroughly studied. It was discovered, that the number of neurons in the inner layer in the range of changes had little effect on efficiency. The volume of training samples reduces efficiency when the value is very small. The influence the of the input training samples dimension on the efficiency was studied; the dimension was reduced from 64 to 16 by eliminating a different set of input parameters. It is established that such changes have a different impact on efficiency, and under certain conditions, the efficiency increases. For a cascade binary network of feed forward propagation and a cascade binary network with reverse propagation of error, it was established that the efficiency changes slightly depending on the number of neurons in the hidden layer in the studied range. A two-layer cascade network with reverse error propagation with a certain combination of neurons has a higher efficiency compared to a single-layer one. The results allow us to outline ways to improve efficiency.