The algorithms of structural identification of static processes with an expert in the regression analysis

Abstract

The method of mathematical modeling of processes (systems) based on passive surveillance of its indicators and their relationship has been considered in the article. Simulation has been considered as one of the tools for the researching of the process structure when the process is represented by a number of inter-related indicators. The aim of the research is to develop methods of mathematical modeling of complex processes (systems) simplifies the task of selecting predictors for parametric identification model. Proposed method of structural modeling of static processes is based on the concept of the graph coherency. The algorithms of model structure forming has been offered. The modeling procedure has been performed with involvement of expert. The features of the model structure determination has been taken into account. The set of process indicators has been divided into two disjoint non-empty subsets − a subset of the independent parameters − predictors and output indicators − functions. The graph of relations parameters of the process has been built on the graph of correlation structure parameters in accordance with algorithms. Has been considered the case where the introduced parameters of quality has been represented by multivariate regression equation and its ultimate best value should correspond to the lowest value. Novelty. The relation of preference has been clarified in the problem of determining the mathematical model structure using correlation and regression analysis with demand positive indicators. Algorithms form the predictor parameters with expert has been developed. Significance. The approach and the algorithms proposed in the paper, allow the use of mathematical modeling methods of complex systems in the context of structural uncertainty.