Choosing operator emotions as feedback for training neural networks

The work is devoted to the study and selection of human emotions with the highest probability of recognition for training neural networks using operator emotions as feedback. On the basis of the presented program, experiments were set up and conducted to study emotions. The following emotions were studied in the work: “anger”, “disgust”, “fright”, ‘happiness”, “sadness”, “surprise” and “neutral emotion”. During the experiments, human emotions were determined, which are recognized by the program with the greatest probability. The average values of the probability of successful or unsuccessful recognition were calculated, and the similarity of emotions was analyzed. Assumptions are made about the use of operator 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 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.