INTELLIGENT ROUTING IN THE NETWORK OF INFORMATION AND TELECOMMUNICATION SYSTEM OF RAILWAY TRANSPORT
DOI:
https://doi.org/10.15802/stp2019/166092Keywords:
information and telecommunication system, ITS, router delay, neural network, NN, sample, activation function, learning algorithm, epoch, errorAbstract
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.
References
Aslanov, A. M., & Solodovnik, M. S. (2014). Issledovanie intellektualnogo podkhoda v marshrutizatsii kompyuternykh setey. Elektrotekhnicheskie i kompyuternye sistemy, 16(92), 93-100. (in Russian)
Bilous, R. V., & Pohorilyi, S. D. (2010). Features of the Application of Genetic Algorithm for Searching Optimal Paths on the Graph. Reiestratsiia, zberihannia i obrobka danykh, 12(2), 81-87. (in Ukrainian)
Kolesnikov, K. V., Karapetyan, A. R., & Kurkov, A. S. (2015). Neural network modelsof data delivery route optimization in dynamic networks. International Scientific Journal, 6, 74-77. (in Russian)
Kolesnikov, K. V., Karapetian, A. R., & Bahan, V. Y. (2016). Analiz rezultativ doslidzhennia realizatsii zadachi marshrutyzatsii na osnovi neironnykh merezh ta henetychnykh alhorytmiv. Visnyk Cherkaskogo derzhavnogo tehnologichnogo universitetu. Seria: Tehnichni nauky, 1, 28-34. (in Ukrainian)
Minimalnoe ostovnoe derevo. Algoritm Kruskala. MAXimal. Retrieved from http://e-maxx.ru/algo/mst_kruskal (in Russian)
Nikitchenko, V. V. (2010). Utility modeliruyushchey sistemy Opnet Modeler. Odessa: Odesskaya nats-ionalnaya akademiya svyazi im. A. S. Popova. (in Russian)
Pavlenko, M. A. (2011). Analysis opportunities of artificial neural networks for solving single-path routing in telecommunication network. Problemy telekomunikatsii, 2(4). Retrieved from http://pt.journal.kh.ua/index/0-139 (in Russian)
Pakhomova, V. M., & Lepekha, R. O. (2014). Analiz metodiv z pryrodnymy mekhanizmamy vyznachennia optymalnoho marshrutu v komp’iuternii merezhi Prydniprovskoi zaliznytsi. Informatsiino-keruiuchi systemy na zaliznychnomu transporti, 4, 82-91. (in Ukrainian)
Pakhomova, V. M. (2018). Doslidzhennia informatsiino-telekomunikatsiinoi systemy zaliznychnoho transportu z vykorystanniam shtuchnoho intelektu: monohrafiia. Dnipro: Standart-Servis. (in Ukrainian)
Pohorilyi, S. D., & Bilous, R. V. (2010). Henetychnyi alhorytm rozviazannia zadachi marshrutyzatsii v merezhakh. Problemy prohramuvannia, 2-3, 171-178. (in Ukrainian)
Bryndas, A. M., Rozhak, P. I., Semenyshyn, N. O., & Kurka, R. R. (2016). Realizatsiia zadachi vyboru optymalnoho aviamarshrutu neironnoiu merezheiu Khopfilda. The Scientific Bulletin of UNFU, 26.1, 357-363. (in Ukrainian)
CiscoTips. Retrieved from http://ciscotips.ru/ospf (in English)
Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66. doi: 10.1109/4235.585892 (in English)
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554-2558. doi: 10.1073/pnas.79.8.2554 (in English)
Chang Wook Ahn, Ramakrishna, R. S., In Chan Choi, & Chung Gu Kang. (n.d.). Neural network based near-optimal routing algorithm, Proceedings of the 9th International Conference on Neural Information Pro-cessing, 2002, ICONIP’02. Singapore. doi: 10.1109/iconip.2002.1198978 (in English)
Kojic, N., Zajeganovic-Ivancic, M., Reljin, I., & Reljin, B. (2010). New algorithm for packet routing in mobile ad-hoc networks. Journal of Automatic Control, 20(1), 9-16. doi: 10.2298/jac1001009k (in English)
Pakhomova, V. M., & Tsykalo, I. D. (2018). Optimal route definition in the network based on the multilayer neural model. Science and Transport Progress, 6(78), 126-142. doi: 10.15802/stp2018/154443 (in Ukrainian)
Schuler, W. H., Bastos-Filho, C. J. A., & Oliveira, A. L. I. (2009). A novel hybrid training method for hopfield neural networks applied to routing in communications networks. International Journal of Hybrid Intelligent Systems, 6(1), 27-39. doi: 10.3233/his-2009-0074 (in English)
Herguner, K., Kalan, R. S., Cetinkaya, C., & Sayit, M. (2017). Towards QoS-aware routing for DASH utilizing MPTCP over SDN, 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). Berlin, Germany. doi: 10.1109/nfv-sdn.2017.8169844 (in English)
Zhukovyts’kyy, I., & Pakhomova, V. (2018). Research of Token Ring network options in automation system of marshalling yard. Transport Problems, 13(2), 145-154. (in English)
Published
How to Cite
Issue
Section
License
Copyright and Licensing
This journal provides open access to all of its content.
As such, copyright for articles published in this journal is retained by the authors, under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). The CC BY license permits commercial and non-commercial reuse. Such access is associated with increased readership and increased citation of an author's work. For more information on this approach, see the Public Knowledge Project, the Directory of Open Access Journals, or the Budapest Open Access Initiative.
The CC BY 4.0 license allows users to copy, distribute and adapt the work in any way, provided that they properly point to the author. Therefore, the editorial board of the journal does not prevent from placing published materials in third-party repositories. In order to protect manuscripts from misappropriation by unscrupulous authors, reference should be made to the original version of the work.
