The concept of a software and analytical complex of the educational process based on ontology and artificial neural networks

Effective management of the learning process of additional professional education programs at the university is conditioned by providing unique needs of students as requested by employers in the real sector of the economy in accordance with the selected competencies and areas of training. At the same time, when solving a number of tasks, the algorithm of which is unknown, there are more and more actively developed and implemented systems using artificial neural networks, which allow classifying and analyzing data for making managerial decisions. Based on such widespread use of artificial neural networks, there is an increasing need for systematization of data to improve the performance of software analytical complex processing, storage, search and analysis of data, for the implementation of training programs at all stages of the life cycle, taking into account uncertainty. The developed software-analytical complex is presented on the example of a model of an intelligent system used to control and analyze the acquired competencies of students, built on the basis of an ontological approach, a model of continuous quality improvement, which makes it possible to determine the interaction of business processes, their sequence and performance benchmarks. To implement this theory, a neural network node scheme of a software analytic complex capable of data-driven learning has been developed. The presented scheme of a neural network node assumes the use of a supervised learning algorithm when a training dataset arrives at the input.