The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures

In the area of logistics management both managers and engineers rely primarily on proven computational algorithms, for this reason, it is often difficult to convince them to the use of artificial neural networks in solving decision problems. The paper presents the possibilities of using the FANN lib...

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Main Authors: Duchaczek Artur, Skorupka Dariusz
Format: Article
Language:English
Published: Sciendo 2015-06-01
Series:Selected Scientific Papers: Journal of Civil Engineering
Subjects:
Online Access:https://doi.org/10.1515/sspjce-2015-0005
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spelling doaj-92163ae67cc54906a7eb7ab5b04ba5f12021-09-05T14:00:46ZengSciendoSelected Scientific Papers: Journal of Civil Engineering1338-72782015-06-01101475610.1515/sspjce-2015-0005sspjce-2015-0005The Use of Artificial Neural Networks to Assess the Capacity of Transport MeasuresDuchaczek Artur0Skorupka Dariusz1General Tadeusz Kościuszko Military Academy of Land Forces, Faculty of ManagementGeneral Tadeusz Kościuszko Military Academy of Land Forces, Faculty of ManagementIn the area of logistics management both managers and engineers rely primarily on proven computational algorithms, for this reason, it is often difficult to convince them to the use of artificial neural networks in solving decision problems. The paper presents the possibilities of using the FANN library in building of a computer application applied in the area of logistics. The possibilities of the component are presented on the example of applications of artificial neural networks to estimate the capacity of transport vehicles based on their dimensions. The example presented in the work was solved with the use of a multi-network Layered Perceptron. The example depicted not only the possibility of using artificial neural networks for solving poorly structured tasks but also practical application of the TFannNetwork componenthttps://doi.org/10.1515/sspjce-2015-0005logisticstransportartificial neural networkscomputer programming
collection DOAJ
language English
format Article
sources DOAJ
author Duchaczek Artur
Skorupka Dariusz
spellingShingle Duchaczek Artur
Skorupka Dariusz
The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures
Selected Scientific Papers: Journal of Civil Engineering
logistics
transport
artificial neural networks
computer programming
author_facet Duchaczek Artur
Skorupka Dariusz
author_sort Duchaczek Artur
title The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures
title_short The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures
title_full The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures
title_fullStr The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures
title_full_unstemmed The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures
title_sort use of artificial neural networks to assess the capacity of transport measures
publisher Sciendo
series Selected Scientific Papers: Journal of Civil Engineering
issn 1338-7278
publishDate 2015-06-01
description In the area of logistics management both managers and engineers rely primarily on proven computational algorithms, for this reason, it is often difficult to convince them to the use of artificial neural networks in solving decision problems. The paper presents the possibilities of using the FANN library in building of a computer application applied in the area of logistics. The possibilities of the component are presented on the example of applications of artificial neural networks to estimate the capacity of transport vehicles based on their dimensions. The example presented in the work was solved with the use of a multi-network Layered Perceptron. The example depicted not only the possibility of using artificial neural networks for solving poorly structured tasks but also practical application of the TFannNetwork component
topic logistics
transport
artificial neural networks
computer programming
url https://doi.org/10.1515/sspjce-2015-0005
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