Applied Research on Generative Artificial Intelligence for Modern Control Systems
Why this work is in the frame
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Bibliographic record
Abstract
Generative artificial intelligence, as a new technology paradigm, has received more and more attention for its powerful generative ability and wide application prospects. Especially in automated control systems, the application of technology based on generative artificial intelligence is gradually becoming a hot spot of research. In this paper, the generative AI automation control system is divided into four levels: input layer, processing layer, instruction generation and control execution layer, and combined with dual encoders, the attention model of multilingual to semantic expression is constructed. Two-dimensional variables are selected to construct a fuzzy PID control system to realize automation control for generative AI system. Comparing the control effects of fuzzy control PID and classical PID, the average errors of the two systems are 1= , 2= respectively. The maximum overshoot and rise time are 9% and 0.08 s, 5% and 0.04 s. The fuzzy PID control effect is more accurate, and at the same time improves the dynamic performance of the system. Analyze the implementation effect on the innovative service application of generative artificial intelligence. Comparing the overall recognition effect of the control system B proposed in this paper, and the two systems with reference to A, their overall recognition effect indexes are 0.94755 and 0.87211, respectively, and the fuzzy PID control system plays an auxiliary enhancement role in the contextual feature recognition of translation services in the intelligent library.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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