利用自动微分和神经网络识别基于能量的滞后模型参数

IF 1.1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Applied Electromagnetics and Mechanics Pub Date : 2023-12-06 DOI:10.3233/jae-230107

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

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引用次数: 0

摘要

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。

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

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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来源期刊

International Journal of Applied Electromagnetics and Mechanics 物理-工程：电子与电气

CiteScore

1.70

自引率

0.00%

发文量

100

审稿时长

4.6 months

期刊介绍： The aim of the International Journal of Applied Electromagnetics and Mechanics is to contribute to intersciences coupling applied electromagnetics, mechanics and materials. The journal also intends to stimulate the further development of current technology in industry. The main subjects covered by the journal are: Physics and mechanics of electromagnetic materials and devices Computational electromagnetics in materials and devices Applications of electromagnetic fields and materials The three interrelated key subjects – electromagnetics, mechanics and materials - include the following aspects: electromagnetic NDE, electromagnetic machines and devices, electromagnetic materials and structures, electromagnetic fluids, magnetoelastic effects and magnetosolid mechanics, magnetic levitations, electromagnetic propulsion, bioelectromagnetics, and inverse problems in electromagnetics. The editorial policy is to combine information and experience from both the latest high technology fields and as well as the well-established technologies within applied electromagnetics.