A Novel Cardiff Model Coefficients Extraction Process Based on Artificial Neural Network

Tian, Mengyue; Bell, James; Azad, Ehsan; Quaglia, Roberto; Tasker, P.J.

doi:10.1109/pawr56957.2023.10046221

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…This suggests that the KBNN model could be used to overcome the extrapolation issues shown for the conventional CM in section II. Also, an ANN-based CM coefficients extractor has been proven to output a set of coefficients with the same level of accuracy as the standard Least Mean Square (LMS) algorithm [7]. Therefore, a novel method is proposed in this paper, taking the advantages from the two model structures.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…However, as a widely used commercially available behavioral model, the CM will provide a simpler polynomial structure and less calculation steps than those of the ANNs when the data complexity increases. Hence, an ANN-based Cardiff Model coefficients extractor [7] is now used. In this step, new CM coefficients sets are extracted using both the measured A-B wave dataset and the FCC ANN models (trained in step 2) predicted B wave dataset, instead of using measured data only.…”

Section: Step 3: Extracting the CM Coefficients With The Ann-based Ex...mentioning

confidence: 99%

“…out of the training data range [6]. Taking this extrapolation advantage into account, the proposed method in this paper involves a combination of a conventional A-B wave-based ANN model and the ANN-based Cardiff Model coefficients extractor [7] to provide novel sets of extracted high order CM coefficients that can offer both accurate interpolation and reasonable extrapolation behavior.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Extraction Method Based on Artificial Neural Network Techniques for Novel Cardiff Model With Reasonable Extrapolation Behavior

Tian,

Bell,

Quaglia

et al. 2024

IEEE Microw. Wireless Tech. Lett.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Step 3: Extracting the CM Coefficients With The Ann-based Ex...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Extraction Method Based on Artificial Neural Network Techniques for Novel Cardiff Model With Reasonable Extrapolation Behavior

Tian,

Bell,

Quaglia

et al. 2024

IEEE Microw. Wireless Tech. Lett.

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“…[15][16][17] To address this challenge, optimization of sampling data have been proposed to cut down the number of measurement data needed. [18][19][20] However, these methods may not fully cover the diversity and complexity of the entire data set. On the other side, using simulation data in place of actual test data may have disadvantages, such as model bias and missing data.…”

Section: Introductionmentioning

confidence: 99%

“…To address this challenge, optimization of sampling data have been proposed to cut down the number of measurement data needed 18–20 . However, these methods may not fully cover the diversity and complexity of the entire data set.…”

Section: Introductionmentioning

confidence: 99%

Knowledge‐based neural network with Bayesian optimization for efficient nonlinearRFdevice modeling

Wang

Xie

et al. 2023

Int J Numerical Modelling

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This paper proposes a novel approach for modeling the nonlinear behavior of microwave devices, with a particular focus on addressing the challenging problem of modeling compound semiconductor transistors under large‐signal excitation. The approach utilizes a knowledge‐based neural network (KBNN) to construct a frequency‐domain behavioral model of the transistor, which maps incident and scattered waves from a coarse model to a fine model in cases where the coarse model fails to match the behavior of a new device. This neural network‐based method aims to achieve better alignment between the extracted model parameters obtained from the coarse model and the behavior of the fine model. To improve mapping efficiency and accuracy, Bayesian optimization techniques are employed to automatically adjust the hyper parameters in the KBNN within a custom‐defined hyper parameter space. The proposed method uses simulation and measurement of a 0.25 um GaN HEMT device operating at 8 GHz, with VGS at −2 V and VDS at 18 V. Furthermore, the proposed model exhibits good interpolation capability at different input power levels, indicating its broad applicability in the design of high‐speed device models. Specifically, the accuracy of the simulation data and test data reached −53.83 and −51.68 dB, respectively, with the test data used in model training being less than 7.5% of the simulation data.

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Development and Evaluation of ANN, ACOR-ANN, ALO-ANN Based Small-Signal Behavioral Models for GaN-on-Si HEMT

Khan,

Husain,

Nauryzbayev

et al. 2023

2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

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A Novel Cardiff Model Coefficients Extraction Process Based on Artificial Neural Network

Cited by 7 publications

References 7 publications

An Extraction Method Based on Artificial Neural Network Techniques for Novel Cardiff Model With Reasonable Extrapolation Behavior

An Extraction Method Based on Artificial Neural Network Techniques for Novel Cardiff Model With Reasonable Extrapolation Behavior

Knowledge‐based neural network with Bayesian optimization for efficient nonlinearRFdevice modeling

Development and Evaluation of ANN, ACOR-ANN, ALO-ANN Based Small-Signal Behavioral Models for GaN-on-Si HEMT

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