A Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction
Bibliographic record
Abstract
When classical particle filter (PF) techniques are used for dynamic system state estimation, they have some limitations: for example, when the weights of simulated samples are not sufficiently large, these classical PFs may suffer from sample impoverishment. In addition, the degraded diversity in sampling particles will limit the estimation accuracy, since the particles cannot capture the entire probability density function (pdf) effectively. To tackle these problems, a mutated PF (MPF) technique is proposed in this paper to approximate the posterior pdf of system states. In the MPF, a novel mutation approach is proposed to search extended areas of the prior pdf using mutated particles to make more comprehensive exploration of the posterior pdf. In addition, a particle selection scheme is suggested in the MPF to detect and process low-weight particles so as to explore the high-likelihood area of the posterior pdf more thoroughly. The effectiveness of the developed MPF technique is verified by simulation tests using a benchmark test model. It is implemented for predicting remaining useful life of batteries. Test results show that the developed MPF can capture a system's dynamics effectively and track system characteristics accurately.
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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".