Taguchi-RBF Neural networks Based Optimization of Phased Array Antenna With Coupling Effects
Bibliographic record
Abstract
In the antenna array synthesis problems, most of the works in literature utilize isotropic elements. Thus, the mutual coupling effects between the array elements are neglected. It is obvious that an array antenna synthesized by neglecting the coupling effects cannot be used in the real world applications due to the possible mismatch between the desired and realized radiation patterns. In this paper, a novel method based on neural network algorithm RBF (Radial Basis Function ) for the synthesis and model of Antipodal Vivaldi antenna with mutual coupling effect is presented. The synthesis in implementation’s method for this type of array permits to approach the appropriated radiation pattern while considering the mutual coupling between its elements. The neural network is used to estimate the array elements’ excitations. The architecture of the neural network based on the radial basis functions (RBFs) is introduced and simulation results are presented. Results show that there is an agreement between the desired specifications and the synthesized one. The proposed optimization approach offers an efficient and robust synthesis procedure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".