Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

GMDH Neural Networks - Based Modeling of Variable Power Inductor

Version 1 : Received: 17 June 2021 / Approved: 18 June 2021 / Online: 18 June 2021 (11:02:08 CEST)

How to cite: Koohi, P.; Mohammadi, P.; Samanbakhah, R.; M.Ibanez, F. GMDH Neural Networks - Based Modeling of Variable Power Inductor. Preprints 2021, 2021060474 (doi: 10.20944/preprints202106.0474.v1). Koohi, P.; Mohammadi, P.; Samanbakhah, R.; M.Ibanez, F. GMDH Neural Networks - Based Modeling of Variable Power Inductor. Preprints 2021, 2021060474 (doi: 10.20944/preprints202106.0474.v1).

Abstract

In this paper, the Group Method of Data Handling (GMDH) type of neural networks is used for the inductance calculation of variable inductors. The relation between the inductance of the inductor in the linear and nonlinear regions is investigated, and parameters such as the voltage across the inductor, bias current, and ac current are taken into account. The experimental setup is used for generating the data needed for training the neural network. Over 800 experiments were conducted and were used for training and validation of the neural network results. The results are compared with the reluctance equivalent circuit method, and they show a much better accuracy. The proposed method can be used for the calculation of various magnetic components, and it is not limited to variable inductors.

Subject Areas

Variable inductor, GMDH – Neural Networks, inductance, magnetic component calculation

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