Comparative analysis on performances of adjustable-gain single-neuron PID controllers based on general fuzzy logic and normal cloud model
Bibliographic record
Abstract
The solutions to parameter setting of PID controllers have always been an essential problem of control system design. The single-neuron PID controller can achieve the parameters' self-adaption to the real operation conditions by adjusting the gain value. Two computational intelligent algorithms deriving their theory sources from the uncertain reasoning are introduced to realize the on-line adjustments of gain, which are general fuzzy logic and a normal cloud model with universality. The designs on these two regulators are given containing the forms of the membership functions under the fuzzy logic & cloud model, control rules for 1-dimensional & 2-dimensional input modes, and the inference models, etc. The numerical simulations are implemented and the comparative analysis on the dynamic performance is presented based on the existent step responses and the adjustable parameters' curves with adaptive changes. By contrast, it concludes that a 2-dimensional normal cloud model leads to the desired overshoot, and its 1-dimensional model with shorter program running time adapts to occasions of high real-time demands, and fuzzy logic regulators can better meet the control requirements of the shorter setting time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".