An automation approach in dielectric constant prediction using machine learning

机译：使用机器学习进行介电常数预测的自动化方法

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This paper aims at predicting dielectric constants of metal with respect to temperature interval at certain frequency using Feed forward back propagation networks, Nonlinear autoregressive exogenous inputs networks and various other algorithms. Calculating the dielectric constant is crucial in the field of engineering; up till now this task is done by manual experiments. Though there are many existing experimental approaches for predicting dielectric constant but no such established algorithms exist that can automate the process of prediction. Through this paper multiple machine learning techniques are applied for predicting dielectric constant; also to improve the correlation between the input attributes optimization is performed.

机译：本文旨在使用前馈传播网络，非线性自回归外源输入网络和各种其他算法来预测金属在一定频率下相对于温度间隔的介电常数。计算介电常数在工程领域至关重要。到目前为止，该任务是通过手动实验完成的。尽管有许多现有的预测介电常数的实验方法，但是尚不存在可以使预测过程自动化的已建立算法。通过本文，多种机器学习技术被应用于预测介电常数。还可以改善输入属性之间的相关性，并执行优化。

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来源
《International Conference on Energy, Communication, Data Analytics and Soft Computing》|2017年|3084-3086|共3页
会议地点 Chennai(IN)
作者
Anindita Das; Arnab Haider; Soumita Samaddar;
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Computer Science Engineering S.V.I.S.T. Kolkata India;

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关键词
Dielectric constant; Prediction algorithms; Predictive models; Mathematical model; Neural networks; Metals; Feeds;

机译：介电常数;预测算法；预测模型；数学模型;神经网络;金属;提要;

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机译：使用机器学习的聚合物的频率依赖性介电常数预测

An automation approach in dielectric constant prediction using machine learning

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