Browsing by Author "Skaballanovich, Tetiana I."

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    Intelligent Routing in the Network of Information and Telecommunication System of Railway Transport
    (Дніпровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2019) Pakhomova, Victoria M.; Skaballanovich, Tetiana I.; Bondareva, Valentyna S.
    ENG: Purpose. At the present stage, the strategy of informatization of railway transport of Ukraine envisages the tran-sition 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 telecommunica-tion 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 net-work 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 ac-cording to different learning algorithms: Levenberg-Marquardt; Bayesian Regularization; Scaled Conjugate Gradi-ent 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.
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    The Use of Neural Network Technologies in Research Competence Formation of IT-specialists for the Railway Industry in Multilevel Training System
    (SWorld in conjunction with D.A. Tsenov Academy of Economics, Svishtov, Bulgaria, 2020) Pakhomova, Victoria M.; Miroshnychenko, Iryna H.; Skaballanovich, Tetiana I.; Bondareva, Valentyna S.
    ENG: The article considers the development of formation methods of research competence with the use of neural network technologies of IT specialists for the railway industry in multilevel training system. The proposed method «ResCompStageNNT» consists of the following stages: the determination of the current load of the MPLS domain tunnels (for the Bachelor’s degree applicants); clustering the traffic flows under the condition of SOM-based QoS parameter and determining the MPLS domain tunnels on the basis of MLP (for the Master’s degree applicants); distribution of the traffic flows by the tunnels of the MPLS domain on the created software package and the organization of a relevant research (for candidates of the Doctor of Philosophy’s degree). The formed research competence of IT specialists of the railway industry provides an opportunity for the ability to research existing and design new computer networks of the information and telecommunication system of railway transport using the neural network technologies. Key words: railway transport, degree, research competence, traffic, routing,