Method for selecting management decision with range input signals for deterministic and stochastic model

Abstract

The article considers the scope of application of control algorithms for a multicomponent dynamic system with uncertain parameters varying depending on a non-stable range flow of input data. The proposed methodology is aimed at simplifying the process of analyzing and selecting data ranges with different levels of relevance used to form a control action. A mathematical model describing the connection between data stocks and flows is presented. This allows identifying the key parameters of the system and focus on their further detailing. The assumption of data transition between classes is based on automatic data update, which reflects the real conditions of the system operation. Initially, data classes do not have a strict hierarchy, but their volume changes depending on time intervals, with the most significant changes occurring near the end of the period under study. These changes are due to internal data movements between classes and external data flows. The expected data flow is calculated based on the initial stock and transition probability, which is consistent with the assumption that flows are proportional to stock. The total number of new data is equal to the number of discarded data.

As a result of comparison of standard algorithmic and coding technologies with the proposed method and subsequent evaluation, the system performance improvement as well as predictability of critical states that can occur at any moment in the presence of uncertain parameters are revealed. A software model for reading and interpreting input signals, identifying and correcting artefacts, processing input data, and testing a multi-component dynamic system with range input data has been implemented