Chaotic Time Series Prediction of Multi‐Dimensional Nonlinear System Based on Bidirectional LSTM Model
Bibliographic record
Abstract
Abstract The current work proposes a hybrid data‐driven model—Convolutional bidirectional long–short term memory (CNN‐BLSTM) for predicting chaotic behavior of three‐coupled Duffing oscillator nonlinear system, in which the CNN is for efficiently extracting the more robust and informative representations of chaotic sequences while the BLSTM is for holding the long‐term dependencies combining the past and future contexts. Different from traditional analytical and numerical approaches, the proposed prediction model features the benefit of focusing on the measured data solely without extensive professional domain knowledge. Additionally, three more recurrent neural network (RNN) models, including simple RNNs, stack LSTMs, and BLSTM, are built and comparisons of generalization performances to the CNN‐BLSTM are conducted. From the findings so far, the CNN‐BLSTM is able to learn the pattern of chaotic time sequence data with less training time and apply the acquired knowledge to the unseen dataset with lower errors. Moreover, the current work decently demonstrates that the proposed model outperforms other three models in terms of stability at different noise levels from two evaluation criteria. The CNN‐BLSTM provides useful guidance for the consideration of predicting multi‐dimensional nonlinear chaotic behavior.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".