An Adaptive Evolving Fuzzy Technique for Prognosis of Dynamic Systems
Bibliographic record
Abstract
The evolving fuzzy technique is a recent development in the soft-computing field that has shown some promising results in applications such as control, classification, and short-term prediction. However, evolving fuzzy techniques still have challenges in terms of high-speed processing of cluster/rule generation, especially in long-term prediction applications due to the broader distribution of the input space. These factors can lead to problems such as overfitting in optimization and high computational costs, which could limit their applications in real-time monitoring. In this article, an adaptive evolving fuzzy (AEF) technique consisting of two novel aspects is developed to tackle these problems. First, an error-assessment method is suggested to monitor the trend of the cumulative training errors and to control the fuzzy cluster evolving process. Second, an adaptive particle filter algorithm is proposed to optimize the fuzzy clusters in order to enhance incremental learning and improve modeling efficiency. The effectiveness of the proposed AEF predictor is verified by simulation tests; it is also implemented for battery remaining useful life forecasting. Test results have shown that the proposed AEF technique can effectively capture the system's dynamic characteristics with fewer rules and can provide more flexibility in fuzzy modeling.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".