Analysis of Neural Networks for Predicting Time Series When Assessing Industrial Safety Risks

Nagibin, Sergey Ya.; Loskutov, Dmitry I.

doi:10.18421/tem92-08

Cited by 1 publication

(1 citation statement)

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“…Currently, a large number of artificial neural networks are known: perceptrons, recurrent networks, convolutional neural networks (CNN), deconvolutional neural networks (DNN), ResNet, and long short term memory network (LSTM). However, they work well in the field of image recognition, face recognition, speech recognition, and translation [17]- [23].…”

Section: Theoretical Basismentioning

confidence: 99%

Neuro-fuzzy-based mathematical model of dispatching of an industrial railway junction

Bogdanova

Nagibin²,

Loskutov³

et al. 2023

Bulletin EEI

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In any transport system, especially at industrial railway junctions, it is fundamentally important to build an effective timetable (traffic schedule) to regulate traffic flows. The task is complicated by the high dimensionality of the railway network of the node, the large number of variable parameters associated with scheduling the use of a traction resource (locomotives) during operation for sorting wagons and transporting payloads (ore, fuel, finished products and empty wagons). The problem is that most plotting problems are NP-hard, i.e. the algorithms for solving them, used to automate the process, may require an unacceptably long execution time by traditional methods of solving this problem (sequential, using reference information; method of thread laying). The article deals with the issues of building a mathematical model for dispatching an industrial railway junction to minimize the time of using locomotives in order to increase the efficiency of its operation. The mathematical model uses the technique of neuro-fuzzy computing to determine the parameters for identifying fuzzy systems and calculating the priorities of operations in the framework of creating a flexible schedule for the decision support system of the dispatching service. The results of modeling and recommendations on the use of the developed methodology are presented.

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Section: Theoretical Basismentioning

confidence: 99%