Reconstruction of Piecewise Chaotic Dynamic Using a Genetic Algorithm Multiple Model Approach
Bibliographic record
Abstract
Reconstruction of chaotic dynamics from its time-series measurement is an important problem for many engineering applications. In this paper, we propose using a novel multiple model (MM) predictor based on a genetic algorithm (GA) to reconstruct piecewise chaotic dynamics. The motivation relies on the observation that conventional single model is usually unable to reconstruct the piecewise dynamics properly because a piecewise map is nonsmooth. In our approach, multiple radial basis function (RBF) neural predictors are used to model the piecewise dynamic in different partition intervals. Switching between different intervals could be estimated by a nonlinear gate model. In particular, a GA is employed here to train the MM and to obtain the optimal RBF parameters. Compared to conventional chaos dynamic reconstruction techniques, the proposed GA-MM method is shown to greatly improve the reconstruction performance for piecewise chaotic dynamics. The superiority is further verified by applying the GA-MM method to model the real-life radar sea-clutter signal obtained from Nova Scotia (NS), Canada, and to predict the electric power pool price time series from Alberta (AB), Canada. Both kinds of real data show that the GA-MM is effective in building a dynamical model. The proposed GA-MM method is also applied to the channel equalization problem of chaos communication systems. Based on the minimum nonlinear prediction error equalization method, it is shown that the GA-MM method has a satisfactory equalization performance even under strong channel effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".