On usage of the neural network technologies in the it- structure components’ diagnosing.

IF 8.2 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY ACS Applied Materials & Interfaces Pub Date : 2024-03-20 DOI:10.15407/jai2024.01.087
Savchuk O., Morgal O.
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Abstract

The idea of using neural network technologes to prove electrophysical diagnostic methods based on the integral physical effects of IT structure components is considered. It is proposed to transform the received information using a discrete Karhunen-Loeve expansion, which gives the minimum root mean square error of packing a priory vectors in multidimensional space. The use of neural networks: MLP, self-organizing (Kohonen Maps) and RBF in MATLAB environment is verified. The best result for microcircuits was obtained using probabilistic RBF-neural networks. A new neural network approach to diagnostics made it possible to perform individual sorting of elements and ststistical evaluation of the IT structure components batch.
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神经网络技术在 IT 结构部件诊断中的应用。
我们考虑了利用神经网络技术来证明基于 IT 结构组件整体物理效应的电物理诊断方法的想法。建议使用离散卡尔胡宁-洛夫扩展对接收到的信息进行转换,该扩展给出了在多维空间中打包优先向量的最小均方根误差。使用神经网络:在 MATLAB 环境中对 MLP、自组织(Kohonen 地图)和 RBF 神经网络的使用进行了验证。使用概率 RBF 神经网络获得了微电路的最佳结果。新的神经网络诊断方法使得对元件进行单独分类和对信息技术结构元件进行批量统计评估成为可能。
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来源期刊
ACS Applied Materials & Interfaces
ACS Applied Materials & Interfaces 工程技术-材料科学:综合
CiteScore
16.00
自引率
6.30%
发文量
4978
审稿时长
1.8 months
期刊介绍: ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.
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