The paper discusses main paths to improve the effectiveness of railway intellectual marshalling yard monitoring and control systems and its land devices. It shows which environment conditions and other factors affect the accuracy of land device measurements particularly park track Occupancy Monitor Block which measures the length of unoccupied track portion. The paper shows that the measurements could be corrected by means of classification algorithms. It is suggested to use hierarchic decision trees as a classification rule. It discusses a method to improve classical decision trees building algorithms based on bootstrap method ideas. The suggested method improves accuracy of built decision trees.
KEYWORDS: Education and training, Diagnostics, Decision trees, Control systems, Intelligence systems, Data mining, Mathematical modeling, Transportation, Artificial intelligence, Classification systems
Modern hump car retarder control systems and their technical diagnostic methods are reviewed. Disadvantages of existing analytical methods of technical diagnostics are revealed. Factors, which help to increase the accuracy of technical diagnostics of car retarders, available in existing hump automated control complexes, are shown. The paper suggests to build a new intelligent technical diagnostic system to improve the accuracy of the current ones. The suggested system will use decision trees, created by means of Data Mining approach under conditions of small training set amount. This approach is based on the ideas of intelligent system self-organization. The increase in accuracy of the decision rules is shown on the verification database examples.
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