Distribution of Information Flows in the Advanced Network of MPLS of Railway Transport by Means of a Neural Model
| dc.contributor.author | Zhukovyts’kyy, Ihor | en |
| dc.contributor.author | Pakhomova, Victoria N. | en |
| dc.contributor.author | Domanskay, Halyna | en |
| dc.contributor.author | Nechaiev, Andrew | en |
| dc.date.accessioned | 2019-11-21T06:40:08Z | |
| dc.date.available | 2019-11-21T06:40:08Z | |
| dc.date.issued | 2019 | |
| dc.description | I. Zhukovyts’kyy: ORCID 0000-0002-3491-5976, V. Pakhomova: ORCID 0000-0001-8346-0405, H. Domanskay: ORCID: 0000-0002-5746-299X | en |
| dc.description.abstract | ENG: Abstract. Ensuring interoperability of railway transport is possible only due to the developed information structure. Today, Ukraine uses the information-telecommunication system (ITS) of railway transport, which is based on a data communication network. The effectiveness of its work is largely determined by the routing system. The current algorithm for choosing the shortest route, which is used in the existing routing protocol (OSPF), does not always lead to an effective result. However, there is MPLS technology, which could improve the quality of the ITS network by creating virtual channels between its nodes. The authors proposed a scheme for selecting tunnels for the flows in the MPLS network, which is based on the neural model of a multilayer perceptron of configuration 18–3–3–10 with the activation function Softmax in hidden layers and a linear activation function in the input layer. To simulate the network operation, flow data is needed: class of service (CoS), sender and recipient identifiers, average flow rate vector and tunnel data (their initial load). The final load of the tunnels is taken as the resulting output of the neural network, on the basis of which the tunnel is selected for the flow of the k-th class of service. | en |
| dc.identifier | DOI: 10.1051/matecconf/201929404007 | |
| dc.identifier.citation | Distribution of Information Flows in the Advanced Network of MPLS of Railway Transport by Means of a Neural Model [Electronic resource] / Ihor Zhukovyts’kyy, Victoria Pakhomova, Halyna Domanskay, Andrew Nechaiev // MATEC Web of Conferences. – 2019. – Vol. 294 : 2nd International Scientific and Practical Conference “Energy-Optimal Technologies, Logistic and Safety on Transport” (EOT-2019). – P. 1–7. – Access Mode: https://www.matec-conferences.org/articles/matecconf/pdf/2019/43/matecconf_eot18_04007.pdf (21.10.2019). – DOI: 10.1051/matecconf/201929404007. | en |
| dc.identifier.uri | http://eadnurt.diit.edu.ua/jspui/handle/123456789/11607 | |
| dc.identifier.uri | https://www.matec-conferences.org/articles/matecconf/abs/2019/43/matecconf_eot18_04007/matecconf_eot18_04007.html | |
| dc.identifier.uri | https://www.matec-conferences.org/articles/matecconf/pdf/2019/43/matecconf_eot18_04007.pdf | |
| dc.language.iso | en | |
| dc.publisher | Dnipro National University of Railway Transport named after Academician V. Lazaryan | en |
| dc.subject | information flows | en |
| dc.subject | railway transport | en |
| dc.subject | neural model | en |
| dc.subject | network of MPLS | en |
| dc.subject | КЕОМ | uk_UA |
| dc.title | Distribution of Information Flows in the Advanced Network of MPLS of Railway Transport by Means of a Neural Model | en |
| dc.type | Article | en |