Adaptive antenna applications by brain emotional learning based on intelligent controller
Bibliographic record
Abstract
This study presents a uniform linear array (ULA) adaptive antenna which uses a theoretical analysis of an on-line autonomous intelligent adaptive tracking controller based on the emotional learning model in mammalian brains. This optimisation approach, called the brain emotional learning based on intelligent controller (BELBIC), demonstrates superior performance in estimating the arrival direction of the incoming signals and performing adaptive beamforming, which is aimed at the receiving end. The most important advantages of this algorithm are its robustness in adaptation and on-line learning ability, which make it suitable for dynamic and real-time applications. In order to investigate the performance of adaptive antenna technology applied in mobile terminals, an appropriate channel model considering the effects of wireless channels is presented. Performance of the BELBIC algorithm is compared with Capon and least mean square (LMS) schemes considering a channel model from the static and dynamic points of view. Simulation results reveal superior performance of the BELBIC approach in almost all the cases. Moreover, the proposed approach demonstrates higher precision and lower computational time in comparison to other classical techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".