Neuro-Transfer Function Model for Neutralization Lines Design of UWB MIMO Antennas
Bibliographic record
Abstract
A multi-output modeling approach that integrates neural networks with transfer functions is proposed for the design of ultra-wideband (UWB) multiple-input multiple-output (MIMO) antennas. The transfer function is introduced to replace the electromagnetic (EM) responses of multiple outputs, thereby decoupling the frequency-dependent information across a broad frequency range. The EM responses of different outputs are solely determined by transfer function parameters and are independent of frequency, significantly reducing the complexity of multioutput modeling over wide band. The proposed high-accuracy, EM-level parametric model can be utilized for surrogate-based optimization or embedded within other multi-objective optimization frameworks. A comprehensive two-stage model training process is presented, along with a validation case involving the modeling of a$2 \times 2$UWB MIMO antenna neutralization line. The results demonstrate that, even with a three-layer multilayer perceptron (MLP) with a small number of neurons and a limited training dataset, the model achieves accurate representations of both$S_{11}$and$S_{21}$. Furthermore, the trained model enables the simultaneous optimization of both outputs, effectively extending the antenna bandwidth while reducing isolation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".