SYNTHESIS AND ANALYSIS OF A STATISTICAL MODEL OF THE DYNAMICS OF TIMES SYSTEMS WITH DISCRETE CONTROL UNDER CONDITIONS OF APRIORI UNCERTAINTY
A statistical model of a times system with discrete control is proposed, which is one of the topical areas of modern decision-making theory. Similar conditions are typical of processing information obtained through remote sensing of natural objects. An effective way to solve the emerging problems is to use structural methods of statistical data analysis, when the state of the system is understood as a set of values of random variables with similar properties. Such a definition makes it possible to simplify the task of estimating the states of the system under study using nonparametric image recognition algorithms, which open up the possibility of predicting their time dynamics on the basis of transformation of random variables sets. The study of the properties of the statistical model of the time dynamics of the objects under consideration is carried out. The probability of changing the system macro-states from initial to the final time intervals is used as an efficiency indicator. The asymptotic properties of the mentioned above efficiency indicator are investigated, and the results are compared with the Markov limiting theorem for chain dependences.
Keywords: time system with discrete control, structural data analysis, nonparametric pattern recognition algorithms, trajectory of the states of the time system, asymptotic properties, estimates of the probability of changing the states of the time system, Markov limiting theorem for chain dependences
