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Modeling of Complex Transport Processes Taking Into Account the Phenomenon of Hysteresis

The problem of improving the accuracy of forecasting complex transport processes of various nature, which are characterized by production and economic indicators, indicators of reliability and safety of transport systems, quality characteristics of transport infrastructure objects in conditions of high uncertainty and data noise, is updated. In particular, the limitations of models for predicting transport processes using time series, regression dependencies, neuro-fuzzy models, and queuing theory are analyzed. Proposed method iterative forecasting of changes in a complex process using models of pattern recognition theory. Models and a method for constructing a recognition system that improve the accuracy of object recognition by taking into account the phenomenon of hysteresis are also proposed. The essence and role of hysteresis in forming the initial data (training set) for identifying the decision rule in the pattern recognition problem are described. The problem of improving the theory of object recognition (predictive classes), taking into account the phenomenon of hysteresis, is solved in two ways: a) introduction of an additional classification feature, taking into account the location of training points on different branches of the hysteresis loop; b)“ breeding classes “ in the existing feature space by adding some constant shift value or sign to the values of the parameters of the points of one of the classes. A practical example illustrating the application of the proposed model construction and the corresponding behavior of the decision maker is considered.