APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO PREDICT RAILWAY SWITCH DURABILITY

The article presents the possibility of applying artificial intelligence to forecast necessary repairs on ordinary railway switches. Railway switch data from Katowice and Katowice Szopienice Północne Stations were used to model neural structures. Using the prepared data set (changes in values of nominal dimensions in characteristic sections of 15 railway switches), we created three variants of railway switch classifications. Then, with the results, we determined the values of classifiers and the low mean absolute error, as well as compared charts of effectivity. It was calculated that the best solution by which to evaluate necessary repairs in railway switches was, in part, to repair the crossing nose. It was assessed that a structure with single output data was more effective for the accepted data.