FORMATION OF THE DATASET AND SELECTION OF PARAMETERS FOR THE BUILDING OF THE ARTIFICIAL NEURAL NETWORKS OF THE ENTERPRISES FINANCIAL SUSTAINABILITY CLASSIFICATION

финансовая устойчивость; искусственная нейронная сеть; классификация; персептрон; функция активации; The purpose . The article is devoted to the preparatory actions preceding of construction of a fully-connected three-layer artificial neural network of direct distribution, which should classify the enterprises for their financial sustainability. Methods . To prepare for the construction of an artificial neural network are presented a range of reasoned judgments and solutions, based on the results of experiments on building a neural network of financial sustainability. The Results . The article presents ways to obtain a dataset for training an artificial neural network of financial sustainability of Ukrainian enterprises. There are presented the optimal architectural parameters and recommendations about the neural network teaching. The attention was paid to such parameters of the artificial neural network as the type of neural network, the error function, the activation function on the hidden and on the initial layers, the learning algorithm, the parameters of initial weight initialization. The application of the recommendations from the article allows constructing an artificial neural network, which classifies enterprises for their financial sustainability with sufficiently high accuracy. Particular attention is paid to selecting neuron activation functions and to the distribution of a general dataset into training, testing and validation subsets. Scientific novelty. The use of 17 financial indicators as factors for modeling financial sustainability is proposed, what should describe this complex concept and allow to obtain maximum accuracy of classification. The article substantiates the use of the construction parameters and training of the neural network, which are best suited for the use of these factors. The practical significance . Using the article`s material will help with building of the artificial neural network of financial sustainability, which can be used to classify enterprises as "financially sustainable" or "potential bankrupt" and can be used by financial and credit institutions, investment funds and state authorities when assessing the risk of bankruptcy of an enterprise. Such a neural network is designed to automate the work of an experienced financial analyst when checking the company for