Adaptive antenna applications by brain emotional learning based on intelligent controller
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it