Multi‐frequency analysis of Gaussian process modelling for aperiodic RCS responses of a parameterised aircraft model

Bilal, Ahmad; Hamza, Syed Muhammad; Taj, Ziauddin; Salamat, Shuaib

doi:10.1049/iet-rsn.2019.0421

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…[8][9][10][11] As a result of its remarkable ability to capture nonlinear behavior, the machine learning (ML) technology, such as artificial neural networks (ANN) [12][13][14][15] and support vector regression (SVR), [16][17][18] is widely used in semiconductor device modeling. GPR [19][20][21][22][23][24][25][26] technology is one of the core methodologies within machine learning methods. The approach serves as an efficient method for developing device models that provide robust prediction capabilities for nonlinear behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the ANN and SVR models, the GPR model determines the probability distribution of all possible acceptable functions, making it more robust to measurement errors or discrete data. [20][21][22] To obtain an accurate behavioral model, the hyperparameters of the GPR method must be adjusted to suit specific modeling conditions, which is a time-consuming and complex process. It is therefore necessary to develop an automatic optimization method for GPR modeling.…”

Section: Introductionmentioning

confidence: 99%

“…The approach serves as an efficient method for developing device models that provide robust prediction capabilities for nonlinear behavior. Unlike the ANN and SVR models, the GPR model determines the probability distribution of all possible acceptable functions, making it more robust to measurement errors or discrete data 20–22 …”

Section: Introductionmentioning

confidence: 99%

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Hyper‐parameter optimizedGPRmodel based on chaos game algorithm forRFpower transistors

Gao,

Zhou,

Crupi

et al. 2024

Int J Numerical Modelling

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In this paper, a new frequency domain behavior model for radio frequency (RF) power transistors based on a hyper‐parameter optimized Gaussian process regression (GPR) method is presented. The chaos game optimization (CGO) algorithm is used to optimize GPR hyperparameters, resulting in the CGO‐GPR model. The basic theory as well as the details of the modeling process are presented. Validation of the model is conducted using a 10‐watt GaN power transistor. Compared to the standard GPR model, the proposed model achieved a significant improvement. Furthermore, the comparison with the particle swarm optimization (PSO) based GPR model (PSO‐GPR) showed that the proposed model allows achieving superior performance, thereby confirming the effectiveness of the developed modeling technique.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyper‐parameter optimizedGPRmodel based on chaos game algorithm forRFpower transistors

Gao,

Zhou,

Crupi

et al. 2024

Int J Numerical Modelling

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show abstract

“…To further improve the performance, a PSO augmented SVR based modelling technique is developed. Finally, Gaussian Process Regression (GPR) technique [42]- [44] is also exploited to model the drain and gate currents. The Bayesian approach is considered herein to model the regression-based problem a non-parametric modeling process.…”

Section: Introductionmentioning

confidence: 99%

Large-Signal Modeling of GaN HEMTs Using Hybrid GA-ANN, PSO-SVR, and GPR-Based Approaches

Jarndal

Husain

Hashmi

et al. 2021

IEEE J. Electron Devices Soc.

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This paper presents an extensive study and demonstration of efficient electrothermal largesignal GaN HEMT modeling approaches based on combined techniques of Genetic Algorithm (GA) with Artificial Neural Networks (ANN), and Particle Swarm optimization (PSO) with Support Vector Regression (SVR). Another promising Gaussian Process Regression (GPR) based large-signal modeling approach is also explored and presented. The GA-ANN addresses the typical problem of local minima associated with the backpropagation (BP) based ANN. The GA successfully aids in the determination of optimal initial values for BP-ANN and enables it to find a unique optimal solution after subsequent of iterations with higher rate of convergence. This is also achieved using PSO-SVR with lower optimization variables. The developed modeling techniques are demonstrated and used to simulate the gate and drain currents of a 2-mm GaN device. All the models are relatively simple, practical, and easy to implement. The gate and drain currents models are embedded in an equivalent large-signal circuit's model and built in Advanced Design System (ADS) software. The implemented model is validated by large-signal measurements and very good fitting results have been obtained. The model also showed an accurate simulation for a nonlinear power amplifier with very good computational speed and convergence.

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Target Simulation of Bucket Reflector Based on Complex Structure

Hao

Wang

Yang³

et al. 2021

2021 International Conference on Applications and Techniques in Cyber Intelligence

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Multi‐frequency analysis of Gaussian process modelling for aperiodic RCS responses of a parameterised aircraft model

Cited by 9 publications

References 21 publications

Hyper‐parameter optimizedGPRmodel based on chaos game algorithm forRFpower transistors

Hyper‐parameter optimizedGPRmodel based on chaos game algorithm forRFpower transistors

Large-Signal Modeling of GaN HEMTs Using Hybrid GA-ANN, PSO-SVR, and GPR-Based Approaches

Target Simulation of Bucket Reflector Based on Complex Structure

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