Adaptive neuro-fuzzy controller based on simplified ANFIS network
Bibliographic record
Abstract
A novel technique used to design a simple version of an adaptive neuro-fuzzy controller (ANFC) is described in this paper. The structure of the proposed Adaptive Simplified Neuro-Fuzzy Controller (ASNFC) consists of reduced numbers of input membership functions (MFs) and consequence parameters (CPs). A Neuro Identifier (NI) is used to track the behaviour of the plant on-line and update the controller. The ASNFC is applied to an SVC device, located at the middle of a single machine infinite bus system (SMIB), to damp power system oscillations. Results of simulation studies demonstrate that the proposed ASNFC provides similar control actions as the ANFC, but with less parameters to optimize. Although the proposed controller is a simplified version of the ANFC, the simulation results obtained show system responses similar to that with the ANFC.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".