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Applying of Neural Networks for Testing of Tracers with using of Empirical Data Cover

Applying of Neural Networks for Testing of Tracers with using of Empirical Data

By: Dariusz Ampuła  
Open Access
|Sep 2019

Abstract

An attempt of designing artificial neural networks for empirical laboratory test results tracers No. 5, No. 7 and No. 8 was introduced in the article. These tracers are applied in cartridges with calibres from 37 mm to 122 mm which are still used and stored both in the marine climate and land. The results of laboratory tests of tracers in the field of over 40 years of tests have been analysed. They have been properly prepared in accordance with the requirements that are necessary to design of neural networks. Only the evaluation module of these tracers was evaluated, because this element of tests, fulfilled the necessary assumptions needed to build artificial neural networks. Several hundred artificial neural networks have been built for each type of analysed tracers. After an in-depth analysis of received results, it was chosen one the best neural network, the main parameters of which were described and discussed in the article. Received results of working built of neural networks were compared with previously functioning manual evaluation module of these tracers. On the basis conducted analyses, proposed the modification of functioning test methodology by replacing the previous manual evaluation modules through elaborated automatic models of artificial neural networks. Artificial neural networks have a very important feature, namely they are used in the prediction of specific output data. This feature successfully used in diagnostic tests of other elements of ammunition.

Language: English
Page range: 5 - 23
Submitted on: Feb 18, 2019
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Accepted on: Sep 6, 2019
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Published on: Sep 30, 2019
Published by: Polish Naval Academy
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Dariusz Ampuła, published by Polish Naval Academy
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.