An Equivalent Dipole Model Hybrid With Artificial Neural Network for Electromagnetic Interference Prediction

Shu, Yufei; Wei, Xing‐Chang; Fan, Jun; Yang, Rui; Yang, Yonggao

doi:10.1109/tmtt.2019.2905238

Cited by 66 publications

(20 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FFNNs are usually used to solve non-dynamic modeling problems [1]. MLPs are the most popularly used FFNN structures, which are widely used in microwave modeling for both passive component modeling [7], [8], [9], [10], [15], [17], [18], [19], [22], [30], [31], [34], [76], [80], [83], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105] and active device/circuit modeling [12], [13], [23], [24], [25], [29], [32], [33], [106], [107], [108], [109], [110], [111], [112], [113], [114], [115],…”

Section: A Feedforward Neural Networkmentioning

confidence: 99%

“…The use of neural networks can help avoid repetitive EM simulations by learning the relationship between EM response and the varying values of geometrical parameters in advance. Many research activities have been devoted to such applications, for example, high-speed interconnects [93], CPW circuit components [94], practical multilayered shielded microwave circuits [124], spiral inductors [45], [95], EM interference (EMI) estimation [96], internally decomposed EM structure [97], metasurfaces [175], differential via holes [46], and couplers [98].…”

Section: A Em Analysis and Parametric Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

This article presents an overview of artificial neural network (ANN) techniques for a microwave computer-aided design (CAD). ANN-based techniques are becoming useful for performing forward/inverse modeling for active/passive components to enhance a circuit design. With measured or simulated data of microwave devices, ANNs can be trained to learn relevant microwave relationships, which are, otherwise, computationally expensive or for which efficient analytical formulas are not available. Fundamental concepts of the ANN structure and training, such as feedforward neural networks (FFNNs), recurrent neural networks (RNNs)/dynamic neural networks (DNNs)/time-delay neural networks (TDNNs), deep neural networks, and neural network training and extrapolation, are described. Knowledgebased neural networks (KBNNs) are described for improving the accuracy and reliability of modeling and design optimization. Various advanced ANN techniques, such as neuro-transfer function (neuro-TF) modeling, neural network inverse modeling, and deep neural network modeling, are discussed. The existing and emerging applications of ANN in microwave CAD are identified, such as electromagnetic (EM)/multiphysics modeling, modeling of nonlinear circuits and transistors, filter design, very largescale integration (VLSI) interconnects, oscillator, transmitter and receiver modeling, and CAD applications in such as gallium nitride (GaN) high electron-mobility transistor (HEMT), wireless power transfer (WPT), microelectromechanical system (MEMS), and substrate-integrated waveguide (SIW).

show abstract

Section: A Feedforward Neural Networkmentioning

confidence: 99%

Section: A Em Analysis and Parametric Modelingmentioning

confidence: 99%

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

show abstract

“…In 2017, Yuan et al used Bayesian networks to predict and evaluate the complex electromagnetic environment [11]. In 2019, Shu et al used Artificial Neural Network (ANN) to predict electromagnetic interference [12]. In 2021, Zhang et al used the GPR algorithm to predict the electromagnetic interference of the UAV dynamic data link [13].…”

Section: Related Workmentioning

confidence: 99%

A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

et al. 2021

View full text Add to dashboard Cite

With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.

show abstract

“…A great part of such works, has been predicated upon stochastic modelling of EMC/EMI problems [2,6,[16][17][18][19]. There is another category of researches, in which simple dipoles have been used to model the radiated emission of the PCB [9,[20][21][22]. In [23], an efficient reciprocity-based algorithm has been introduced which evaluates the radiated immunity of PCBs by extracting the worst-case ports' voltages and currents of a multi-conductor transmission-line network, induced by an incident plane wave.…”

Section: Introductionmentioning

confidence: 99%

Analytical Determination of the Maximum Absorbable Interference Power Delivered to PCB Components

Mohammadi¹,

Dogaheh²,

Khavasi³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p> In this paper we derive a method to determine the worst-case radiated immunity of a given printed circuit board (PCB) layout, whose benefits are threefold. First, the worst-case performance could be used as a reference to compare the radiated immunity of two competing, schematically-identical-yet-electromagnetically-distinct layouts. Second, it could be used as a criterion to determine whether a given layout is able to observe the particular requirements of a given electromagnetic compatibility (EMC) standard. And finally, since this method is element-independent, it requires no modelling for complex circuit elements such as non-linear elements, logic gates, processors, etc. to interact with electromagnetic interference (EMI). Our method determines how much power could be delivered to the non-linear circuit elements of the PCB by an EMI under a worst-case scenario. This could be done, either for the entire PCB, or for specific parts and elements of interest, such as sensitive elements, logic gates, microprocessors, etc. While a fully analytical approach with a closed-form solution is developed for the former, the latter could be obtained through an analytical derivation followed by numerical maximization. These methods would enable the rapid and straightforward evaluation of the designs’ immunity at any stage, resulting in saving cost and time. </p>

show abstract

An Equivalent Dipole Model Hybrid With Artificial Neural Network for Electromagnetic Interference Prediction

Cited by 66 publications

References 21 publications

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

Analytical Determination of the Maximum Absorbable Interference Power Delivered to PCB Components

Contact Info

Product

Resources

About