PSO and GSA algorithms for fuzzy controller tuning with reduced process small time constant sensitivity
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Bibliographic record
Abstract
This paper discusses implementation aspects related to a Particle Swarm Optimization (PSO) algorithm, a Gravitational Search Algorithm (GSA) and a hybrid PSO-GSA. These evolutionary optimization algorithms are applied to the optimal tuning of Takagi-Sugeno-Kang PI-fuzzy controllers (T-S-K PI-FCs) for a class of nonlinear second-order processes with an integral component. The parameters of T-S-K PI-FCs are variables in the optimization problems with objective functions which depend on the output sensitivity function with respect to the small time constant of the process, and T-S-K PI-FCs with a reduced sensitivity with respect to the process small time constant are proposed. A comparison of PSO, GSA and PSO-GSA from algorithms' accuracy point of view is presented in the framework of a case study accompanied by digital simulation results.
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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 | 0.000 |
| 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