Neural Network Model for Predicting the Performance of a Transport Task

Shevchuk, D. O.; Yakushenko, Oleksandr; Pomytkina, Liubov; Medynskyi, Denys; Shevchenko, Yulia

doi:10.1007/978-981-33-6208-6_27

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Problems of transport modelling were considered in particular by Antonova et al [5; 6], Yakushenko [7] and Shevchuk [8].…”

Section: Literature Review and Defining The Problemmentioning

confidence: 99%

Implementation of the mathematical model of interaction of passenger transport modes for sustainable economic development

Vasylenko,

Ozerova,

Pron

et al. 2024

MATEC Web Conf.

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As part of a research, the study of the level of solving the scientific problem of transport flow management in large cities was carried out as a component of the integrated information-intelligent transport system of the city. A significant amount of research on a number of important components of the problem has been identified, including the application of new approaches, methods and information technologies for planning and forecasting traffic flows, optimization of bimodal urban networks. The software implementation of a mathematical model to optimize the interaction of urban passenger transport, passenger transfer points and urban trains in major transportation hubs in terms of sustainable development and digitalization was proposed. A toolkit for the integrated interaction of city train, urban passenger transport and passenger transfer points in major cities has been implemented. As a result of the study, a high level of versatility of the proposed model and the possibility of its active practical application was determined.

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“…Problems of transport modelling were considered in particular by Antonova et al [5; 6], Yakushenko [7] and Shevchuk [8].…”

Section: Literature Review and Defining The Problemmentioning

confidence: 99%

Implementation of the mathematical model of interaction of passenger transport modes for sustainable economic development

Vasylenko,

Ozerova,

Pron

et al. 2024

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…The scientific gap of the art-on-the-day investigations contains the lack of the uncertainty degree conditional optimization problem settings that relate with the statistical estimations in the aviation radio equipment reliability parameters monitoring [13], novelty smart materials creation [14], neural network modelling targeted at the transportation challenging tasks performance prediction [15]. The entropy ideas could be applicable to the neuro-fuzzy network synthesis [16] optimization as well.…”

Section: Introductionmentioning

confidence: 99%

Basic Theoretical Provisions of Entropy Approach for Intelligent Air Transportation Management

Goncharenko

2023

Computer Modeling and Intelligent Systems

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The paper is dedicated to the elaboration of the principal theoretical provisions for the optimal governing of the intelligent air transportation management system. Subjective entropy maximum principle developed for active systems control is an initial postulate. The optimization theory of the subjective individuals' preferences is the working hypothesis. Based upon the Jaynes' principle of the entropy maximum the preferences functions are being found in the explicit view. Conditional optimization of the subjective preferences functions entropy, as the uncertainty measure modeling the available operational multialternativeness, allows organizing some rational air transportation system's managerial efforts. Illustrative example simulation is performed. Necessary diagrams are plotted.

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“…The economical issues are also should be taken into account [13]. Aviation field of industry is a bright example for the combined research [14][15][16]. Such approach has already been probated with the studies presented in the references of [17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Material Tolerable State Maximum Probability Timing: The Elements of the Uncertainty Measure Conditional Optimization Doctrine

Goncharenko

2022

MSF

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The presented study demonstrates the possibility of an analytical approach called the multi-optional hybrid-effectiveness functions uncertainty measure conditional optimization doctrine. This method is applied in order to obtain the known, therefore considered proven, solutions by-passing the entirely probabilistic approach. It is made in application to the stochastic process measures determination. Supposedly, the process is developing in the aircraft engine system’s element (the state condition of its material). The solution is found in the framework of the entropy paradigm. It helps assess the newly emerged materials applications rationality and the novelty technologies implementations expediency on condition of the operational options uncertainty.

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Neural Network Model for Predicting the Performance of a Transport Task

Cited by 5 publications

References 11 publications

Implementation of the mathematical model of interaction of passenger transport modes for sustainable economic development

Implementation of the mathematical model of interaction of passenger transport modes for sustainable economic development

Basic Theoretical Provisions of Entropy Approach for Intelligent Air Transportation Management

A Material Tolerable State Maximum Probability Timing: The Elements of the Uncertainty Measure Conditional Optimization Doctrine

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