A firefly-based particle filter technique for system state estimation and battery RUL prediction
Bibliographic record
Abstract
Abstract Accurate measurement and estimation of state variables in nonlinear dynamic systems are critical in various engineering and scientific applications. While particle filter (PF) techniques have become increasingly popular for modeling such dynamic systems, they are prone to sample degeneracy and impoverishment, which can considerably affect performance. Existing PF methods often come with drawbacks like high computing costs and noise sensitivity. This paper presents a novel probabilistic posterior optimization particle filter (PPO-PF) technique, inspired by the firefly algorithm, to address these PF limitations and enhance PF efficiency. In the proposed PPO-PF technique, an adaptive search method is proposed to locate the high-likelihood region within the posterior space for improved convergence. A new step size selection method is suggested to facilitate navigation within the search space. In addition, a particle position optimization approach is proposed to guide low-weight particles towards high-probability regions, optimizing the posterior probability density function and mitigating sample degeneracy. The proposed PPO-PF technique is validated by simulation under various model conditions, including predicting the remaining useful life of Lithium-ion (Li-ion) batteries. The results indicate that the proposed PPO-PF reliably captures system dynamics with improved accuracy, even under high noise conditions. These findings highlight its potential to enhance measurement science by offering a more reliable approach for state estimation in complex systems.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".