Neurofuzzy Reinforcement Learning Control Schemes for Optimized Dynamical Performance
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Bibliographic record
Abstract
Tracking control mechanisms employ optimization approaches that rely on tracking error signals to advise appropriate control decisions. However, these processes often neglect other important criteria, such as the control effort required to optimize the overall dynamic performance and the response's transient characteristics. A fuzzy control mechanism is developed to fulfill the aimed tracking objectives. Then, it is integrated with two other supporting artificial intelligence schemes to optimize the overall performance during the tracking process. One is based on a Q-learning approach, while the other uses a neural network architecture. Both methods are tested to adjust the main fuzzy tracking control signal to minimize energy dissipation within the dynamical system. These supporting mechanisms revealed improvements over the standalone tracking fuzzy system. The fuzzy-neural network approach, which is based on an optimized future dynamical cost function, exhibited superior results compared to the fuzzy-Q-learning technique.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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