2025 , Volume 30, № 1, p.132-146

The paper highlights an application of the main ideas of optimal experimental design theory during collection of data for training of an artificial neural network. The main purpose of this work is to demonstrate possible effect of a matter of choice for the experimental design applied to model training process and model accuracy. It is necessary to determine the experimental design that is the closest to the optimal one. For this purpose, Q-optimality criterion is used. This criterion assumes using neural network response function which is introduced in this research. The study describes a process of initial dataset generation based on an experimental design that consists of two spectrum points. With the help of the obtained datasets, a binary classification pro blem is sorted out. To solve the problem a feed-forward neural network with one hidden layer is used. The research results revealed that the increase of distance between experimental design spectrum points leads to the increase of neural network accuracy. Also, the effect of data normalization on the model fit process is shown. For some experimental designs, data normalization allows significant speeding up neural network training. In addition, based on the obtained results it is concluded that choosing the experimental design that is the closest to the optimal one can obviously increase the artificial neural network accuracy and reduce its training time