Selection of Operator’s Emotions as Feedback Neural Network Training

The work is devoted to the study and selection of a person’s emotion with the highest probability of recognition for training neural networks using the operator’s emotions as feedback. On the basis of the presented program, experiments were set up and carried out to study emotions. The following emotions were studied in the work: “angry”, “disgust”, “scared”, “happy”, “sad”, “surprised”, “neutral”. In the course of the experiments, human emotions were identified that are most likely to be recognized by the program. The average values of the probability of successful or unsuccessful recognition were calculated, and the similarity of emotions was analyzed. Assumptions are made about using the operator’s emotions as feedback for training neural networks. The problem of reducing the time for training a neural network aimed at solving socially significant economic problems is being solved. It is assumed that the approach will expand the scope of neural networks in non-core industries by reducing the requirements for the operator/programmer and computing resources.