Development of a model selection tool based on numerical set analysis for rehabilitation using robotic technologies

Abstract

Decision-making systems are considered in which the task of selecting a model arises as a tool for significant information compression. The problem of substantiating and creating quantitative artificial intelligence (AI) tools for automatic model selection preceding approximation is analyzed. The sequence of actions of the process of evaluating mutually related data sets is formed, which is brought to codimensional conditions that are determined by the accuracy class of the devices, the measurement method, and the method of reducing models to a single form. The generalized model reduces alternative models of the selection set to a single form. Analytically determined characteristic constants allow us to study, compare them between groups, and classify the dynamics of assessing reliability and adequacy as integral indicators before the approximation begins. This tool for analyzing the suitability of the model for description, which is based only on the results of data from related numerical sets, is an AI tool. An analytically quantitative assessment of the interval of existence of the permissible value of characteristic constants is presented. The relationship between the magnitude of their boundaries for the model within the permissible values of the relative error suitable and unsuitable for description based on the properties of quadratic norms, which determines the maximum possible error, has been established. The formed toolkit for quantitative proof of the best membership of the model type was analyzed for the description of experimental data. The examples show that according to a set of definitions, the deviations of the characteristic constants are synchronous with the relative error and adequacy of the model. The high resolution of the adequacy indicator of 10-3 and the range, which is more than 40-60 times the max/min ratio, will be useful for program algorithms as an indicator.