利用 BP 神经网络对毕菲尔德-布朗效应进行数学建模和认证

Yan Zhang, Xiangyu Cheng
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引用次数: 0

摘要

在毕菲尔德-布朗效应的力计算中,要精确确定非对称电容器在任意形状情况下产生的升力大小是不可行的。本文首先推导出一个普遍适用的公式,解决了电荷分布不均匀时的升力问题。其次,通过基于相似性原理的尺寸分析实验方法,我们计算了所有类型非对称电容器的升力。最后,我们通过实验获得了一组推力数据,然后分别通过推导出的数学模型和 BP 神经网络对这组实验数据进行拟合。这证实了数学模型的准确性。
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Mathematical Modeling and Certifying for Biefeld–Brown Effect with BP Neural Network
In the force calculation of the Biefeld–Brown Effect, it is not feasible to precisely determine the magnitude of the lift force generated by an asymmetric capacitor in a scenario where its shape is arbitrary. In this paper, first, we deduce a universally applicable formula, it solves the problem of the lift force with uneven charge distribution. Second, by the experimental method of dimensional analysis based on the principle of similarity, we calculate the lift forces of all types of asymmetric capacitors. Finally, we obtain a set of thrust data through experiments, and then fit the set of experimental data through the derived mathematical model and BP neural network, respectively. It confirms the accuracy of the mathematical model.
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来源期刊
International Journal of High Speed Electronics and Systems
International Journal of High Speed Electronics and Systems Engineering-Electrical and Electronic Engineering
CiteScore
0.60
自引率
0.00%
发文量
22
期刊介绍: Launched in 1990, the International Journal of High Speed Electronics and Systems (IJHSES) has served graduate students and those in R&D, managerial and marketing positions by giving state-of-the-art data, and the latest research trends. Its main charter is to promote engineering education by advancing interdisciplinary science between electronics and systems and to explore high speed technology in photonics and electronics. IJHSES, a quarterly journal, continues to feature a broad coverage of topics relating to high speed or high performance devices, circuits and systems.
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