Cognitive intelligent control. Part I: System of operator emotions’ assessment with application of deep machine learning based on soft computing
The article consists of two parts. The first part considers the possibilities of application of intelligent computing technologies in the form of deep machine learning. Software-algorithmic support for machine learning is based on the knowledge base optimizer on soft computing, using the SCOptKB ™ software toolkit. The knowledge base optimizer is based on three genetic algorithms, implements the optimal structure of a fuzzy neural network and plays the role of a universal approximator of the training signal with required accuracy. The application of this approach in the tasks of cognitive intelligent control with the use of a cognitive helmet as a neurointerface is considered. The purpose of this work is to show experimentally the possibility of mental states’ classifying for a human operator, to reveal objective indicators of the psychophysiological state of the investigated person using intelligent computing technologies such as soft computing. The role and necessity of application of intelligent computing for the task of describing the general psychophysiological state of a human operator is shown with examples. The developed information technologies are considered on special (difficult for diagnostic practice) examples of assessing the emotional state of children with autism spectrum disorders. The history of the creation of knowledge bases for an intelligent service robot is also presented.