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

Dariusz Ampuła
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引用次数: 1

Abstract

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.
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基于经验数据的神经网络在示踪剂检测中的应用
摘要本文介绍了一种针对5号、7号、8号实验室验货结果示踪剂设计人工神经网络的尝试。这些示踪剂应用于口径从37毫米到122毫米的药筒中,这些药筒仍然在海洋气候和陆地环境中使用和储存。分析了40多年来野外示踪剂实验室试验的结果。它们已经按照设计神经网络所必需的要求进行了适当的准备。仅对这些示踪剂的评估模块进行了评估,因为该测试元素满足了构建人工神经网络所需的必要假设。已经为每种分析示踪剂建立了数百个人工神经网络。在对收到的结果进行深入分析后,选择了最佳的神经网络,并对其主要参数进行了描述和讨论。将神经网络构建的工作结果与这些示踪剂的先前功能手动评估模块进行比较。在进行分析的基础上,通过阐述人工神经网络的自动模型,提出了功能测试方法的修改,以取代以往的人工评估模块。人工神经网络有一个非常重要的特点,即用于预测特定的输出数据。这一特点已成功地用于弹药其他成分的诊断试验。
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