A PSO Accelerated Immune Particle Filter for Dynamic State Estimation
Bibliographic record
Abstract
Particle Filter (PF) is a flexible and powerful Sequential Monte Carlo (SMC) technique to solve the nonlinear state/parameter estimation problems. The generic PF suffers due to degeneracy or sample impoverishment, which adversely affects its performance. In order to overcome this issue of the generic PF, a Particle Swarm Optimization accelerated Immune Particle Filter (PSO-acc-IPF) is proposed in this work. It combines the robustness and the diversified search capability of the Immune Algorithm (IA) and the speed and the computational efficiency of the Particle Swarm Optimization (PSO) in pursuing the global optimal solution. Mutation plays the key role in the proposed algorithm to help avoid the local optima and search for a global best solution. A two stage mutation operation is proposed. The first stage, with a high mutation rate, helps in exploring a larger solution space and the second stage, with a smaller mutation rate, helps in local optimal search. Later on, PSO is employed to accelerate the convergence speed. To validate the effectiveness of the proposed algorithm, its performance is compared with the generic PF and PSO Particle Filter (PSO-PF). The simulation results have demonstrated better robustness in state estimation for switching dynamic 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".