Utilizing neural networks in magnetic media modeling and field computation: A review

Abstract

Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

“…An ANN is an intelligent nonlinear mapping system built to loosely simulate the functions of the human brain and has been considered as a nonlinear regression analysis tool capable of approximating any sort of arbitrary function [13]. Owing to their flexibility as function approximators, ANNs are robust methods in tasks related to time-series forecasting [14].…”

Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India

“…However, previous pandemics have often shown complex and nonlinear patterns with time, and therefore a linear approach might not yield the best results. Artificial Neural Networks (ANN) have emerged as one of the most successful methods to overcome this limitation of nonlinearity [47][48][49][50]. However, ANN models are not capable of capturing both linear as well as nonlinear features of the time series equally well [51], and thus several hybrid methodologies have been developed [52][53][54][55].…”

Vector Hysteresis Processes for Innovative Fe-Si Magnetic Powder Cores: Experiments and Neural Network Modeling

“…The components of the magnetic field, computed by the couple of NNs, are collected by the multiplexer Mj and finally sent to the output. The vector model was inspired by the one proposed in [23], however, it differs in terms of the architecture and formulation of the single sub-networks.…”

“…In addition to the experimental verification, a computationally efficient vector hysteresis model based on feedforward neural networks (NNs) was utilized to reproduce the behavior of the test sample and to predict power losses. The use of neural networks in simulating hysteresis phenomena has been extensively studied in the literature, mostly for scalar problems [17][18][19][20][21][22][23]. Indeed, NN-based approaches are computationally efficient simulation tools and can be easily formulated in either direct (H input-B output) or inverse (B input-H output) form.…”

“…In most cases the memory storage mechanism is taken into consideration, exploiting feedback algorithms such that the previous value of the model output (or some other information about the "past history") is also given as an input. Other strategies to account for the memory storage property are based on the recurrent NNs, with increased model complexity, as reported in [23], but they are not considered in this work.…”