Employing automatic differentiation and neural networks for parameter identification of an energy based hysteresis model

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-12-06 DOI:10.3233/jae-230107

Eniz Museljic, K. Roppert, L. Domenig, Alice Reinbacher Köstinger, M. Kaltenbacher

引用次数: 0

Abstract

This paper is about the parameter identification of an energy based hysteresis model from measurements by employing automatic differentiation and neural networks. We first introduce the energy based hysteresis model and the parameters which are to be identified. Then we show how the model can benefit from automatic differentiation. After that we incorporate a parametrization of the energy based hysteresis model via distribution functions and identify the parameters of the distribution function. Then, the hysteresis model is sampled and the generated datasets are used to train neural networks to predict the hysteresis parameters. The described methods are tested and verified on synthetic as well as measurement data.

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利用自动微分和神经网络识别基于能量的滞后模型参数

本文研究了利用自动微分和神经网络对基于能量的滞后模型进行参数辨识的方法。首先介绍了基于能量的磁滞模型和需要辨识的参数。然后，我们将展示该模型如何从自动微分中获益。然后通过分布函数对基于能量的迟滞模型进行参数化，并确定分布函数的参数。然后，对迟滞模型进行采样，生成的数据集用于训练神经网络来预测迟滞参数。所描述的方法在合成和测量数据上进行了测试和验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.