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The work is aimed at solving a relevant for Ukraine and the world scientific and applied problem – the development of methods and means of constructing a psychophysical portrait of man to determine its stress resistance. For a number of professions, special methods of selecting personnel for special abilities are needed. Such abilities (qualities) include: health; motivation and will to act decisively, with the maximum effort of their physical, intellectual and psychological capabilities; physical training; level of mastery of technical means (or other means necessary in a critical situation); temperament; nature; emotional condition; accumulation of fatigue during the task and others. The article develops a method for classifying users according to the level of stress resistance on the basis of a modified auto-associative neural network using the Fletcher – Reeves algorithm. There are several ways to classify stress responses, but a more obvious study is to divide them into behavioral, intellectual, emotional and physiological manifestations of stress. The main signs of behavioral stress are psychomotor disorders (excessive muscle tension, winter breathing rhythm), lifestyle changes (changes in daily routine, sleep disturbances), occupational disorders (decreased productivity, increased fatigue), impaired social role functions, increased conflict, increasing aggression, etc.). In conditions of constant influence of stress factors on human consciousness, the decision can be ill-considered and made under the influence of emotions. To avoid such a situation in the workplace, the head of the company must monitor the condition of the operator. This human operator stress assessment system has been developed to predict human operator stress. The main task of the model is to predict based on the obtained parameters of the human condition (stress or not). Additional testing was added to increase the model’s performance based on input data on physical parameters, gender, age, height, and bad habits. The purpose of testing is to increase the accuracy of model prediction. Data from 92 respondents with 1180 records were selected for testing. Data set division: testing – 20 %, training / verification – 80%. The best results were achieved with the help of our own developed neural network based on Keras. Also in the paper the implementation of decision-making methods, the method of k-nearest neighbors and the modified auto-associative network was presented.

DEVELOPMENT OF A USER CLASSIFICATION METHOD ACCORDING TO THE LEVEL OF STRESS RESISTANCE USING A MODIFIED AUTO-ASSOCIATIVE NEURAL NETWORK