Probabilistic modeling for dynamic processes

Abstract

The probability of event occurrence can be determined by calculation, however for complex processes, accounting for multiple states is so complex that the probability of the final event cannot be determined differently than by collecting statistical data. This reduces the ability to predict outcomes in any process simulation, which affects the quality of advanced systems. The development of methods for predicting the probability of occurrence of an event when considering complex dynamic processes is a pressing task, the solution of which will help to improve the quality of modeling of technological processes and, as a result, to increase the efficiency of designing machines implementing them. The authors proposed a probability modeling method for dynamic processes, which is a special way of applying probability theory and is workable if the change in the parameters analyzed corresponds to the normal law of distribution. A study carried out on the analysis of the process of sowing corn seeds by vacuum sowing made it possible to predict with reliability more than 95% the probability of formation of “zero seed fodders” depending on their physical and mechanical properties, as well as parameters and adjustment modes of operation of the device.