INTELLIGENT ROUTING IN THE NETWORK OF INFORMATION AND TELECOMMUNICATION SYSTEM OF RAILWAY TRANSPORT

Authors

DOI:

https://doi.org/10.15802/stp2019/166092

Keywords:

information and telecommunication system, ITS, router delay, neural network, NN, sample, activation function, learning algorithm, epoch, error

Abstract

Purpose. At the present stage, the strategy of informatization of railway transport of Ukraine envisages the transition to a three-level management structure with the creation of a single information space, therefore one of the key tasks remains the organization of routing in the network of information and telecommunication system (ITS) of railway transport. In this regard, the purpose of the article is to develop a method for determining the routes in the network of information and telecommunication system of railway transport at the trunk level using neural network technology. Methodology. In order to determine the routes in the network of the information and telecommunication system of railway transport, which at present is working based on the technologies of the Ethernet family, one should create a neural model 21-1-45-21, to the input of which an array of delays on routers is supplied; as a result vector – build tags of communication channels to the routes. Findings. The optimal variant is the neural network of configuration 21-1-45-21 with a sigmoid activation function in a hidden layer and a linear activation function in the resulting layer, which is trained according to the Levenberg-Marquardt algorithm. The most quickly the neural network is being trained in the samples of different lengths, it is less susceptible to retraining, reaches the value of the mean square error of 0.2, and in the control sample determines the optimal path with a probability of 0.9, while the length of the training sample of 100 examples is sufficient. Originality. There were constructed the dependencies of mean square error and training time (number of epochs) of the neural network on the number of hidden neurons according to different learning algorithms: Levenberg-Marquardt; Bayesian Regularization; Scaled Conjugate Gradient on samples of different lengths. Practical value. The use of a multilayered neural model, to the entry of which the delay values of routers are supplied, will make it possible to determine the corresponding routes of transmission of control messages (minimum value graph) in the network of information and telecommunication system of railway transport at the trunk level in the real time.

Author Biographies

V. M. Pakhomova, Dnipro National University of Railway Transport named after Academician V. Lazaryan

Dep. «Electronic Computing Machines», Dnipro National University of Railway Transport named after Academician V. La-zaryan, Lazaryan St., 2, 49010, Dnipro, Ukraine, tel. +38 (056) 373 15 89, e-mail viknikpakh@gmail.com

T. I. Skaballanovich, Dnipro National University of Railway Transport named after Academician V. Lazaryan

Dep. «Electronic Computing Machines», Dnipro National University of Railway Transport named after Academician V. La-zaryan, Lazaryan St., 2, 49010, Dnipro, Ukraine, tel. +38 (056) 373 15 89, e-mail sti19447@gmail.com

V. S. Bondareva, Dnipro National University of Railway Transport named after Academician V. Lazaryan

Dep. «Electronic Computing Machines», Dnipro National University of Railway Transport named after Academician V. La-zaryan, Lazaryan St., 2, 49010, Dnipro, Ukraine, tel. +38 (056) 373 15 89, e-mail bond290848@gmail.com

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Published

2019-05-06

How to Cite

Pakhomova, V. M., Skaballanovich, T. I., & Bondareva, V. S. (2019). INTELLIGENT ROUTING IN THE NETWORK OF INFORMATION AND TELECOMMUNICATION SYSTEM OF RAILWAY TRANSPORT. Science and Transport Progress, (2(80), 77–90. https://doi.org/10.15802/stp2019/166092

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Section

INFORMATION AND COMMUNICATION TECHNOLOGIES AND MATHEMATICAL MODELING