Method for estimating the state of a nonlinear system based on logical data analysis
Through studies of real systems from various application domains there are, besides the issues related to obtained results interpretability and classification, the issues of obtaining complex evaluation of nonlinear system state with account for values of all its characteristics and their totality. The correctness of nonlinear system state evaluation allows improvement of its behavior prediction accuracy and obtainment of interpretable results for recommendations and decision making. In this work, a nonlinear system state evaluation method based on machine learning method, logical data analysis, is considered. The proposed method is based on the search for patterns and the construction of classifiers and allows consideration of all the characteristics of a nonlinear system, their totality and the relationship between them. It has been demonstrated that this nonlinear system state evaluation method makes it possible to analyze and obtain patterns for incomplete and inaccurate data, which is a common occurrence in real complex nonlinear systems. This method can be used in the design and implementation of intelligent forecasting environments, the formation of recommendations and decision making for automating the processes of medical diagnostics, the study of the behavior of biological, economic and social nonlinear systems. The proposed reduction of patterns based on their recurrent conjunction allows obtaining optimal classifiers with high interpretability and generalization, which increases the correctness of nonlinear system state evaluation and, as a result, the accuracy of behavior prediction, recommendations and decision making.