Hardware implementation of a novel inference engine for interval type-2 fuzzy control on FPGA
Bibliographic record
Abstract
Interval type-2 fuzzy logic controllers (IT2 FLCs) have shown a promising potential in handling uncertainties compared to their type-1 counterparts, and as a result, we have witnessed increasing usage of IT2 FLCs in various applications. Due to the complex structures of IT2 FLCs, using them in real-time applications might be computationally expensive. To facilitate real-time implementation of these controllers, hardware with parallel processing abilities are recommended; field-programmable gate arrays (FPGA) are one class of such hardware. In this paper, we propose a structure for implementing a new IT2 FLC inference mechanism called BMM [2] — that has been recently introduced in the literature — on an FPGA. We first demonstrated how the proposed structure can be implemented on software; next, we proposed an implementation architecture for the IT2 FLC mechanism on hardware. We performed simulations and experiments on two different plants and compared the speed of our controllers. The performance speed as well as the tracking of our proposed control structure in simulations and experiments were shown to be very close to each other. Using the BMM engine for the proposed hardware structure proves to be faster than other existing controllers in the literature. Thus, it is expected that IT2 FLCs can be easily implemented on hardware to further enable their real-time applications.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".