Development of a Trainable Classifier of State of Rail Lines with Multiple Patterns of Image Recognition

Abstract

Introduction. The necessity to classify the state of rail lines affected by significant damaging factors on the sensitive element of the information sensor providing the assurance of classification quality with the required length of the rail lines of the control section forms the task of creating a classifier with extended functionality. Extending the functionality is possible using multidimensional state images with a set of informative features and training procedures for classification models. Using the classical classification principle with a single model leads to an excessive complication of the classification algorithm with low accuracy due to inaccurate solution of the system of conditional equations with multidimensional approximation by Hermite polynomials. Materials and Methods. The principles of reducing the dimension of the features space, various procedures for trainable classifier of state of rail lines with multidimensional patterns, the selection of decisive classification rules with a hierarchical grouping of classes, and the formation of a set of models of varying degrees of complexity trained to solve an incompatible system of equations are considered to solve the problem. There were obtained various degrees of complexity used in the adaptive algorithm for classifying the rail lines states using Hermite polynomials as models. Results. The article presents the results of developing 57 classifier models using Hermite polynomials with features of 2, 3, 4, 5, 6 arguments. As an example, the procedure of developing models with 2–6 features is shown. The research results showed that with an increase in the number of features, the quality of classification improves, as when dividing the state space into several classes. Discussion and Conclusion. The results of the studies confirm the feasibility of the principle of classification of rail line states by a set of classification models, and an algorithm of recursively increasing the classification complexity using a model of increased complexity. The criterion for presenting a new, more complex model is the mismatch between the results of the class calculation by the i-th model and the real class in which the rail line is located at the moment in time.